While the automation of overhead cranes is progressing, the ‘lift-off’ process is still manually operated. In this paper, we developed a system for estimating the angle and slackness of lifting wire using point cloud data obtained by a depth camera that can be easily installed on actual equipment. We then developed a lift-off operation support system where subjects only monitor the estimated wire angle and slackness. The experimental results using a scale-model crane show that the support system suppressed the swing of the suspended load, compared to a lift-off operation with the subject’s vision.

The camera that monitors bus drivers’ glance behavior and pose must be placed behind the steering-wheel to avoid obstructing driver’s view. The problem is that the steering-wheel occludes part of driver’s body, which would reduce the accuracy of driver’s pose estimation. In this study, we propose an approach to estimate the steering-wheel area by using semantic segmentation and remove this area from the image by using Image Inpainting. Our experimental results showed that the accuracy of pose estimation could be improved and using Pluralistic Image Completion (PIC) for removing the steering wheel had the best results. In addition, the experimental results showed that adjusting the variable z of PIC before driving can detect posture with higher accuracy.

Robot systems with both "intrinsic safety" and "high output" are expected to be implemented in heavy-duty industries such as construction and manufacturing. But there are currently no effective solutions. In our previous work, a backdrivable piston using magnetorheological fluid (MRF) whose viscosity can vary with the applied magnetic field was developed. However, it was a passive device, and its active control scheme was not proposed. The design was complex enough to hinder its implementation to robot arms. In this article, we develop an easily implementable rotary actuator using MRF and propose a basic controller. The actuator was designed based on a vane motor and was driven by hydraulic oil (i.e., MRF), so it can generate high torque. It also has a built-in MRF valve in the vane which can change the output torque and backdrivability with the magnetic field controlled by coil current. Each component was designed to maximize the dynamic range of output torque and backdrivability based on a multiphysics coupling model of electromagnetics, MRF magnetization, and fluid dynamics. Moreover, three basic control modes including backdrivable, high-response, and power-efficiency modes were designed and tested. The experimental results showed that the proposed MRF actuator had both high output and intrinsic backdrivability, and control modes could show the advantages of the MRF actuator.

To smoothly and efficiently enable autonomous mobile robots to move in human crowd environments, path planning based on human movement-prediction, reactive movement toward human movement, and active motion-inducing physical interaction to avoid getting stuck are required. In this study, we developed a reactive, proactive, and inducible proximal crowd navigation (PCN) method that is based on a newly developed inducible social force model (i-SFM). The PCN first reactively generates multiple paths including avoidance, proximal, and inducible physical-touch paths based on the waypoint-i-SFM fused-path planning method, proactively predicts human movement based on i-SFM, selects an optimal path based on the movement cost including robot-movement efficiency and the crowd-invasion index (impulsive and touch forces applied to the humans), and finally reactively moves in the crowd based on the forces applied on it. The proposed PCN method works not only when the robot enters crowds but also when it is already in a crowd and avoids humans who enter the crowd, so we also applied our PCN method to both situations. Simulation and experimental results indicated that our PCN method can adequately predict the movement of human crowds and achieve higher movement efficiency without stuck, compared with a conventional non-proximal navigation methodusing the SFM.

The underground facilities require to be inspected regularly for long term and safe-use. Several mobile inspection systems are widely used, but some crucial problems with the limited inspection distance still exist due to the unstable wireless communication and cable restrictions in complex facilities. To solve these problems, we propose a wheeled robot chain control system (RCCS) for underground facilities inspection based on visible light communication (VLC) and solar panel receivers. The RCCS consists of a leader and follower robot and can extend the inspection range with special "optical signal relay" technology. VLC can provide not only illumination for inspection but also less attenuation, higher information security, and stronger antielectromagnetic interference capability, compared with conventional radio communication in complex underground facilities. Using solar panels can increase the receiving area of optical signals, environmental adaptability, and system robustness, compared with conventional photodiode (PD) receivers. Moreover, to ensure longer and more stable communication, we develop an optical signal relay mechanism along-side an attenuation control module. We performed several experiments to evaluate the signal waveform, communication quality, effective communication region, and cooperative movement. The results indicated that the proposed system could extend the inspection range with higher communication quality and environmental adaptability than VLC-based system with PD receivers.

It has been reported that the driver workload is a cause of drowsiness, fatigue, and accidents of the driver, and a system for estimating the driver workload is required. In the conventional study, we have developed a system that estimates the current and future driver workload by using LSTM (Long Short-Term Memory). However, this system used an ambiguous subjective evaluation index for the teacher label, which had the problem of adversely affecting the discrimination accuracy. Therefore, in this study, we propose a system that can estimate the current and future driver workload with high accuracy by removing the ambiguity of subjective evaluation using semi-supervised learning and logistic regression. The experimental results show that the proposed system is superior in discrimination accuracy to the conventional study.

While the automation of overhead travelling cranes is progressing, the ‘dynamic lift off’ process is still manually operated. In this paper, we thus developed an automatic ‘dynamic lift off ’system based on a trolly-position control system using wire tension. The system suppresses swaying of the suspended load after the dynamic lift off by correcting the suspension position so that the combined direction of the tension of each suspension wire matches the gravity direction of the suspended load. As a result of the experiment using the physical simulation using Unity and the developed scale model, we confirmed that the sway of the suspended load can be suppressed by using the automatic trolly-position control system than the manual operation.

It is important for autonomous personal mobility(PM) to be able to move in narrow spaces. In this study, we develop a trajectory planning method that takes into account the unique motion model of differtenial-wheeled PMs, e.g., longitudinal body size, inability to move sideways, and inner wheel difference. It inlcudes the use of forward and backward movement, lateral movement by switchback, and sweling movement before turning at a corner. We compared the proposed method with a general path planning method (dynamic window approach) and confirmed that the proposed method does not get stuck.

A treadmill has a potential to enable humans to have a walking experience in remote place without a spatial limitation for telepresence. In this study, we present a treadmill system with a string traction, which enables a human to walk in the similar way to a level ground gait during the transition phase to a steady state from an acceleration state. As a result of an experiment with six users, the proposed treadmill system enabled the users to walk in an acceleration state with the same head acceleration pattern as with level ground walking while canceling the inertial effect by the string traction, which means that the users was able to experience walking in the transition to a steady state from an acceleration state on the treadmill.

Vision plays an important role in motion planning for mobile robots which coexist with humans. Because a method predicting a pedestrian path with a camera has a trade-off relationship between the calculation speed and accuracy, such a path prediction method is not good at instantaneously detecting multiple people at a distance. In this study, we thus present a method with visual recognition and prediction of transition of human action states to assess the risk of collision for selecting the avoidance target. The proposed system calculates the risk assessment score based on recognition of human body direction, human walking patterns with an object, and face orientation as well as prediction of transition of human action states. First, we investigated the validation of each recognition model, and we confirmed that the proposed system can recognize and predict human actions with high accuracy ahead of 3 m. Then, we compared the risk assessment score with video interviews to ask a human whom a mobile robot should pay attention to, and we found that the proposed system could capture the features of human states that people pay attention to when avoiding collision with other people from vision.

Abstract

Self-healing properties of robots can aid in achieving a high level of motion continuity despite the absence of manual maintenance. Therefore, various studies have been conducted on self-healing materials and mechanisms to incorporate self-healing properties in robots. However, the self-healing performance of a motor rotation system, which is the power source of existing robots, has not been realized owing to the unsuitability of the self-healing method and material strength. Therefore, we propose a self-healable torque transmission mechanism using a low-melting-point metal that can be applied to transmission elements because of its strength and rigidity. Additionally, heating for self-healing can be performed without contact through induction heating. Hence, a self-healable torque transmission mechanism with a simple structure can be applied to a motor drive system where continuous rotation occurs. We evaluated the performance of the proposed mechanism experimentally by measuring the transmittable torque and the amount of energy absorbed when the torque transmission is interrupted. The results verify that the healing performance and energy absorption of the proposed mechanism remain stable, and the mechanism can heal without any performance degradation. Furthermore, the proposed mechanism was implemented in a robot to demonstrate its practical applications. It was found that this mechanism enables the robot to re-operate by self-repair even if it receives a load that can destroy the joint due to overload, and the robot’s ability to continue motion could thus be improved.

This article explores a scheme of tuning a basic input-output (I/O) gain (BIOG) depending on usage conditions for improving the work performance of human-operated machines. I/O gain tuning is effective for improving operability and workability, but continual and/or huge changes makes operators confuse the machine dynamics, and then degrade the operability. Thus, the proposed tuning system tunes the BIOG at long terms according to comprehensive characteristics of the work content and operator, collected from a control lever input histogram. The target value is specified by a Gaussian distribution, meaning that all ranges of the spring-type control lever are equally used. This approach was chosen because leveling the control lever histogram independently of the operator and work content provides a consistent operational experience, leading to comfortable operations. In this article, BIOG curves are specified as a polygonal line involving a break and saturation point. These points are adjusted by reciprocal conversion of differential areas between the Gaussian distribution and obtained histogram curves. Experimental results obtained with a robot arm indicated that the developed BIOG tuning system could improve time efficiency by decreasing the differential area and increase the subjective usability as compared to a conventional fixed BIOG system. Moreover, the relationship between the histogram and BIOG curve could reveal features of both the work content and the operator's skill level.

Disaster response robots are expected to perform complicated tasks such as traveling over unstable terrain, climbing slippery steps, and removing heavy debris. To complete such tasks safely, the robots must obtain not only visual-perceptual information (VPI) such as surface shape but also the haptic-perceptual information (HPI) such as surface friction of objects in the environments. VPI can be obtained from laser sensors and cameras. In contrast, HPI can be basically obtained from only the results of physical interaction with the environments, e.g., reaction force and deformation. However, current robots do not have a function to estimate the HPI. In this study, we propose a framework to estimate such physically interactive parameters (PIPs), including hardness, friction, and weight, which are vital parameters for safe robot-environment interaction. For effective estimation, we define the ground (GGM) and object groping modes (OGM). The endpoint of the robot arm, which has a force sensor, actively touches, pushes, rubs, and lifts objects in the environment with a hybrid position/force control, and three kinds of PIPs are estimated from the measured reaction force and displacement of the arm endpoint. The robot finally judges the accident risk based on estimated PIPs, e.g., safe, attentional, or dangerous. We prepared environments that had the same surface shape but different hardness, friction, and weight. The experimental results indicated that the proposed framework could estimate PIPs adequately and was useful to judge the risk and safely plan tasks.

Conventional soft robotic grippers have been developed based on soft pneumatic actuators or using smart soft materials, but they need external pressure sources, are easily deteriorated or used in millimetric-scale, which leads to increase of the independency, instability, and vulnerability. In this study, we propose an EPM-MRE (Electropermanent Magnet-Magnetorheological elastomer) actuation system, which strengthens the independency and stability of soft actuation by non-contact magnetic force. We established its fundamental principle and investigated parameter design through developing a suction cup as a robotic gripper. We prototyped an EPM-MRE actuated suction cup based on magnetic-charge and tensile modeling as well as optimized the structure of EPM by introducing axisymmetric EPM with frustum pole and suction cup by introducing bi-silicone structure to uniformly activate MRE membrane. The activation of EPM by different current pulses and suction force affected by contact shapes and air gaps were tested. Evaluations showed that the suction cup could be activated with 10 ms and generate a maximum suction force of 9.2 N at a steady state with 0 J energy consumption. In conclusion, the EPM-MRE soft actuation system can be used as a robotic gripper.

We propose a tool-use model that enables a robot to act toward a provided goal. It is important to consider features of the four factors; tools, objects actions, and effects at the same time because they are related to each other and one factor can influence the others. The tool-use model is constructed with deep neural networks (DNNs) using multimodal sensorimotor data; image, force, and joint angle information. To allow the robot to learn tool-use, we collect training data by controlling the robot to perform various object operations using several tools with multiple actions that leads different effects. Then the tool-use model is thereby trained and learns sensorimotor coordination and acquires relationships among tools, objects, actions and effects in its latent space. We can give the robot a task goal by providing an image showing the target placement and orientation of the object. Using the goal image with the tool-use model, the robot detects the features of tools and objects, and determines how to act to reproduce the target effects automatically. Then the robot generates actions adjusting to the real time situations even though the tools and objects are unknown and more complicated than trained ones.

In semi-autonomous vehicles (SAE level 3) that requires drivers to takeover (TO) the control in critical situations, a system needs to judge if the driver have enough situational awareness (SA) for manual driving. We previously developed a SA estimation system that only used driver's glance data. For deeper understanding of driver's SA, the system needs to evaluate the relevancy between driver's glance and surrounding vehicle and obstacles. In this study, we thus developed a new SA estimation model considering driving-relevant objects and investigated the relationship between parameters. We performed TO experiments in a driving simulator to observe driver's behavior in different position of surrounding vehicles and TO performance such as the smoothness of steering control. We adopted support vector machine to classify obtained dataset into safe and dangerous TO, and the result showed 83% accuracy in leave-one-out cross validation. We found that unscheduled TO led to maneuver error and glance behavior differed from individuals.

Controlling minimum toe clearance (MTC) is considered an important factor in preventing tripping. In the current study, we investigated modifications of neuro-muscular control underlying toe clearance during steady locomotion induced by repeated exposure to tripping-like perturbations of the right swing foot. Fourteen healthy young adults (mean age 26.4 ± 3.1 years) participated in the study. The experimental protocol consisted of three identical trials, each involving three phases: steady walking (baseline), perturbation, and steady walking (post-perturbation). During the perturbation, participants experienced 30 tripping-like perturbations at unexpected timing delivered by a custom-made mechatronic perturbation device. The temporal parameters (cadence and stance phase ), mean, and standard deviation of MTC were computed across approximately 90 strides collected during both baseline and post-perturbation phases, for all trials. The effects of trial (three levels), phase (two levels: baseline and post-perturbation) and foot (two levels: right and left) on the outcome variables were analyzed using a three-way repeated measures analysis of variance. The results revealed that exposure to repeated trip-like perturbations modified MTC toward more precise control and lower toe clearance of the swinging foot, which appeared to reflect both the expectation of potential forthcoming perturbations and a quicker compensatory response in cases of a lack of balance. Moreover, locomotion control enabled subjects to maintain symmetric rhythmic features during post-perturbation steady walking. Finally, the effects of exposure to perturbation quickly disappeared among consecutive trials. %

Controlling minimum toe clearance (MTC) is considered an important factor in preventing tripping. In the current study, we investigated modifications of neuro-muscular control underlying toe clearance during steady locomotion induced by repeated exposure to tripping-like perturbations of the right swing foot. Fourteen healthy young adults (mean age 26.4 +/- 3.1 years) participated in the study. The experimental protocol consisted of three identical trials, each involving three phases: steady walking (baseline), perturbation, and steady walking (post perturbation). During the perturbation, participants experienced 30 tripping-like perturbations at unexpected timing delivered by a custom-made mechatronic perturbation device. The temporal parameters (cadence and stance phase%), mean, and standard deviation of MTC were computed across approximately 90 strides collected during both baseline and post-perturbation phases, for all trials. The effects of trial (three levels), phase (two levels: baseline and post-perturbation) and foot (two levels: right and left) on the outcome variables were analyzed using a three-way repeated measures analysis of variance. The results revealed that exposure to repeated trip-like perturbations modified MTC toward more precise control and lower toe clearance of the swinging foot, which appeared to reflect both the expectation of potential forthcoming perturbations and a quicker compensatory response in cases of a lack of balance. Moreover, locomotion control enabled subjects to maintain symmetric rhythmic features during post-perturbation steady walking. Finally, the effects of exposure to perturbation quickly disappeared among consecutive trials.

Gait phase detection, which detects foot-contact and foot-off states during walking, is important for various applications, such as synchronous robotic assistance and health monitoring. Gait phase detection systems have been proposed with various wearable devices, sensing inertial, electromyography, or force myography information. In this paper, we present a novel gait phase detection system with static standing-based calibration using muscle deformation information. The gait phase detection algorithm can be calibrated within a short time using muscle deformation data by standing in several postures; it is not necessary to collect data while walking for calibration. A logistic regression algorithm is used as the machine learning algorithm, and the probability output is adjusted based on the angular velocity of the sensor. An experiment is performed with 10 subjects, and the detection accuracy of foot-contact and foot-off states is evaluated using video data for each subject. The median accuracy is approximately 90% during walking based on calibration for 60 s, which shows the feasibility of the static standing-based calibration method using muscle deformation information for foot-contact and foot-off state detection.

Gait phase detection, which detects foot-contact and foot-off states during walking, is important for various applications, such as synchronous robotic assistance and health monitoring. Gait phase detection systems have been proposed with various wearable devices, sensing inertial, electromyography, or force myography information. In this paper, we present a novel gait phase detection system with static standing-based calibration using muscle deformation information. The gait phase detection algorithm can be calibrated within a short time using muscle deformation data by standing in several postures; it is not necessary to collect data while walking for calibration. A logistic regression algorithm is used as the machine learning algorithm, and the probability output is adjusted based on the angular velocity of the sensor. An experiment is performed with 10 subjects, and the detection accuracy of foot-contact and foot-off states is evaluated using video data for each subject. The median accuracy is approximately 90% during walking based on calibration for 60 s, which shows the feasibility of the static standing-based calibration method using muscle deformation information for foot-contact and foot-off state detection.

For the safety, underground facilities are required to be inspected regularly, especially with image analysis. Traditional wireless and wired transmission techniques have a weakness of limited transmission range in narrow underground environments. In this study, a new image transmission method based on visible light communication (VLC) has been thus proposed. Two types of detectors as an image signal receiver have been tested and discussed in the following experiments. The photodiodes (PDs) are widely used as a common image signal detector in VLC technology, but image signal detection using solar panels (SPs) has not been studied. PDs have a higher sensitivity and faster response time but a limited detection area and high cost. Besides, PDs require the lens to focus light. On the other hand, SPs have much larger optical signal receiving areas and stronger optical signal capture capabilities. They can realize lens-free detection and are inexpensive. These features of PD were firstly verified in experiments with several receiving areas and angles of detectors. The experimental result revealed that PD had better image detection and recovery capabilities than those of SP. Then, we found that a larger receiving area obtained by using double PDs/SPs improved the brightness of the restored image. In a supplementary experiment, the influence of different RGB optical components on VLC, especially the VLC-based image transmission, has been investigated by using two-dimensional Fourier transform frequency analysis. We found that the red optical component significantly increased the intensity and energy of the restored image as the image low-frequency signals were larger than the restored image using ordinary mixed white light, and moreover, the blue optical component decreased the low-frequency part of the image.

With the increasing use of backpacks on a daily basis, appropriate assessment of shoulder load, which has adverse effects on the body, has become more important. We focused on nociceptive pain, which is a physiological warning signal, and performed a subjective evaluation of loading conditions. In this study, we investigated the relationship between multi-point mechanical stimuli set at38 measuring points on the shoulder, and overall pain. In the experiment, eight subjects rated their pain levels at 24 loading conditions (combinations of 3 weight, 2 weight-distance, 2 weight-height, and 2 padding conditions) using a pain scale. In the statistical analysis, the overall pain intensities at different loading conditions were compared through ANOVA, and weight and distance from body were confirmed as main contributing factors. In the regression analysis, four different models were used to fit the overall data. A generalized linear model (GLM) with polynomial sigmoid function resulted in the best fit. GLM fitting was also performed on the data after these have been divided into 8 groups based on combinations of distance-height-padding. The independent variables, the selected combinations of loads at the measuring points, differed depending on the loading conditions. For more accurate regression, loads that contribute to the determination of overall pain intensity should be appropriately selected according to the loading conditions. These results can be used to comprehensively evaluate backpack design based on shoulder pain.

When using a backpack, proper shoulder load reduction is required. We focused on pain (nociceptive pain), which is a warning signal to protect the human body, and we aimed to extract the shoulder parts to avoid heavy loads while walking with a backpack. We set 19 measuring points on each shoulder and 12 measuring points on the lower back. Using three-axis tactile sensors, we then recorded the interface load on the shoulders and lower back under two back panel conditions: general flat panel and panel with lumbar pad. With 180 data for mean load and 150 data for peak load on each measuring point, we confirmed the load distribution and load shift effects using a lumbar pad by comparing the shoulder load and the lower back load. Then, the shoulder load data was normalized by the pain threshold for a single-point pressure stimulus at each measuring point of the subject. The pain threshold was estimated by an approximate expression with a sigmoid function for pain scores, which were collected by subjective evaluation with a pain scale. In statistical analysis, through multiple comparisons (Steel-Dwass test) for the mean values of the normalized shoulder load on each measuring point and its mean value of the entire shoulder, we extracted seven potential high-risk points (coracoid process, medial and lateral part of the clavicle regions, medial and lateral part of the ridgeline of the shoulder, and supraspinatus). Moreover, we observed that high-risk loads remained locally behind a significant reduction of the entire shoulder load with a lumbar pad. These results can be used to improve backpack design for proper loads on the shoulder.

An ability of visually-guided and anticipatory adjustments of locomotion corresponding to upcoming obstacles is important to avoid trip-induced fall. For establishing gait training based on visually-guided and anticipatory adjustments, techniques reproducing realistic training environment are essential. Although some previous works proposed virtual obstacles using mixed reality, the feasibility of virtual obstacles encouraging people to perform realistic obstacle negotiation on a treadmill, where gait training is usually conducted, is still unclear. In this study, we investigated toe heights when stepping over the obstacle in both cases of virtual and real obstacles during walking on the treadmill. Five participants stepped over two types of mixed reality boxes and real boxes, with box placements close and distant from them. The results generally indicate that the toe heights of the leading foot tended to be similar between mixed reality and real obstacles in cases where the obstacle was located distant from participants, a condition that enabled participants to predict when obstacles approached. However, the toe heights of the trailing foot tended to be lower when stepping over the MR obstacles than the real obstacles. We discuss the feasibility and shortcomings of the future use of MR HMDs as replacement for traditional gait training setup.

For personal mobility(PM) operators, it is difficult to move through a crowd and it is important to estimate collision risks and to control driving velocity properly. In this study, we propose an automatic velocity control system for PM based on “Passability Index (PI),” which indicates difficulty of driving while pedestrians are passing. The system uses an RGB-D camera and LRFs to estimate a pedestrian’s position and velocity. To calculate PI, the system calculates width of passage during passing considering pedestrian’s movement. Then, PM adopts a proper passing velocity depending on PI. Experimental results show that the proposed system helped operators to drive safer compared to manual (non-supported) driving.

The purpose of this study is to develop a basic control system that enhances mechanical and geometric adaptability for magnetorheological fluid (MRF) robot arms with high backdrivability and high output power, which we have developed in a previous study. A prototype control system was developed to control the force of the endpoint by controlling pressure in the actuator. The results of gravity compensation and surface copying motion experiments were conducted. The experimental results showed that proposed controller could effectively control the MRF robot arm.

The multi-degree-of-freedom disaster response robot ‘OCTOPUS’ requires an ‘environment-adaptive motion generation function’ to fully utilize its advanced ‘body.’ The disaster site is unknown and complex, so there is a limit to the amount of control rules that can be described in advance by humans. To address this issue, we have developed a basic framework for an adaptive reinforcement system that learns behaviors in a virtual space generated based on environmental information acquired in a real space. In this study, we develop a motion learning system based on task classification and reuse of learned control rules. As a result of experiments on rough terrain, we confirmed that the proposed system could accomplish a moving task on any unknown terrain in a shorter time than the conventional system by the task classification and reusing control laws.

Although exists several drivers' gaze direction classifiers to prevent traffic accidents caused by inattentive driving, making this classification while the driver's face is temporarily or permanently occluded remains exceptionally challenging. For example, drivers using masks, sunglasses, or scarves and daily light variations are non-ideal conditions that recurrently appear in an everyday driving scenario and are frequently overlooked by the existing classifiers. This paper presents a single camera framework gaze zone classifier that operates robustly even during non-uniform lighting, non-frontal face pose, and faces undergo temporal or permanent occlusions. The usage of a normalized dense aligned face pose vector, the classification result of a pre-processed right eye area pixels, and the classification result of a pre-processed left eye area pixels is the cornerstone of the feature vector used in our model. The key of this paper is double-folded: firstly, the usage of a normalized dense alignment for a robust face, landmark, and head-pose direction detection and secondly, the processing of the right and left eye images using computer vision and deep learning techniques for refining, modifying, and finally labeling eyes information. Experiments on a challenging dataset involving non-uniform lighting, non-frontal face pose, and faces with temporal or permanent occlusions show each feature's importance towards making a robust gaze zone classifier under unconstrained driving situations.

The skin is an important organ which enables humans to interact with the unstructured environment around. It is perfectly soft and covers the entire body providing immediate feedback even when that part is not directly in the field of vision. With the human skin as an inspiration, in this paper, we develop a novel completely soft robot skin for tactile sensing. The skin utilizes a new type of material called as Permanent Magnet Elastomer (PME) to replace the traditionally used hard permanent magnet for hall effect based tactile sensors. PME is formed by mixing Neodymium particles in a polymer base and using strong magnetization (up to 6 T) for anisotropy and to achieve strong and complete magnetization. The 6-axis soft PME is a perfect replacement for powerful hard magnets. We also do a thorough analysis of this material by infusing it in different types of silicone and as a result the most suitable combinations are selected. Performance tests show that the sensor can detect minute forces like 0.1 N. Moreover, the hysteresis test is carried out and the hysteresis error for our skin is found to be only 1.402%. An overloading test is also performed by loading the skin up to 64 N to check the robustness. In conclusion, the skin can produce reliable Triaxial force measurements and we present two models of it for smaller and large force range measurements respectively.

The lack of sleep (typically <6 hours a night) or driving for a long time are the reasons of drowsiness driving and caused serious traffic accidents. With pandemic of the COVID-19, drivers are wearing masks to prevent infection from it, which makes visual-based drowsiness detection methods difficult. This paper presents an EEG-based driver drowsiness estimation method using deep learning and attention mechanism. First of all, an 8-channels EEG collection hat is used to acquire the EEG signals in the simulation scenario of drowsiness driving and normal driving. Then the EEG signals are pre-processed by using the linear filter and wavelet threshold denoising. Secondly, the neural network based on attention mechanism and deep residual network (ResNet) is trained to classify the EEG signals. Finally, an early warning module is designed to sound an alarm if the driver is judged as drowsy. The system was tested under simulated driving environment and the drowsiness detection accuracy of the test set was 93.35%. Drowsiness warning simulation also verified the effectiveness of proposed early warning module.

Nowadays, the use of electric vehicles is increasing leading to a growing demand for more efficient use of lithium-ion batteries. The state-of-charge (SOC) has been estimated in previous studies to optimize energy management of batteries. For more efficient battery utilization, detecting degradation is important. However, it is difficult for conventional methods to distinguish the effect of the model parameters including different time constants. Identifying model parameters of multiple RC parallel branches, which represent the impedance of wider frequency ranges, is a necessary requirement to detect the degradation of parts. In this study, we present a method for estimating the model parameters of multiple RC parallel branches. We designed the Markov Chain Monte Carlo algorithm by setting a search range limit and moving window, which enable estimation of the model parameters of parallel branches of different time constants. Through validation of the algorithm based on simulation, the model parameters of a third-order circuit were estimated to be within the error range of 15.2 %. In addition, impedance was calculated from the estimated model parameters in the test using a real battery dataset. The error of impedance was less than 10 % from 0.01 to 100 Hz which was sufficiently low to monitor the change of the parameters owing to degradation. As the impedance in the high-frequency band above 0.1 Hz is more likely to change because of degradation, the proposed method can be used to monitor the model parameters that change as a result of degradation.

Professional drivers are required to safely transport passengers and/or properties of customers to their destinations, so they must keep being mentally and physically healthy. Health problems will largely affect driving performance and sometimes cause loss of consciousness, which results in injury, death, and heavy compensation. Conventional systems can detect the loss of consciousness or urgently stop the vehicle to prevent accidents, but detection of symptoms of diseases and providing support before the driver loses consciousness is more reasonable. It is challenging to earlier detect symptoms with high confidence. Toward solving these problems, we propose a new method with a multi-sensor based driver monitoring system to detect cues of symptoms quickly and a verbal interaction system to confirm the internal state of the driver based on the monitoring results to reduce false positives. There is almost no data that records abnormal conditions while driving and tests with unhealthy participants are dangerous and ethically unacceptable, so we developed a system with pseudo-symptom data and did outlier detection only with normal driving data. From data collection experiments, we defined the confidence level derived from cue signs. The results of evaluation experiments showed that the proposed system worked well in pseudo headache and drowsiness detection scenarios. We found that signs of drowsiness varied with individual drivers, so the multi-sensor based driver monitoring system was proved to be effective. Moreover, we found that there were individual differences in how the cue signs appeared, so we can propose an online re-training method to make the system adapt to individual drivers.

For the safety, underground facilities are required to be inspected regularly, especially with image analysis. Traditional wireless and wired transmission techniques have a weakness of limited transmission range in narrow underground environments. In this study, a new image transmission method based on visible light communication (VLC) has been thus proposed. Two types of detectors as an image signal receiver have been tested and discussed in the following experiments. The photodiodes (PDs) are widely used as a common image signal detector in VLC technology, but image signal detection using solar panels (SPs) has not been studied. PDs have a higher sensitivity and faster response time but a limited detection area and high cost. Besides, PDs require the lens to focus light. On the other hand, SPs have much larger optical signal receiving areas and stronger optical signal capture capabilities. They can realize lens-free detection and are inexpensive. These features of PD were firstly verified in experiments with several receiving areas and angles of detectors. The experimental result revealed that PD had better image detection and recovery capabilities than those of SP. Then, we tbund that a larger receiving area obtained by using double PDs/SPs improved the brightness of the restored image. In a supplementary experiment, the influence of different RGB optical components on VLC, especially the VLC-based image transmission, has been investigated by using two-dimensional Fourier transform frequency analysis. We found that the red optical component significantly increased the intensity and energy of the restored image as the image low-frequency signals were larger than the restored image using ordinary mixed white light, and moreover, the blue optical component decreased the low-frequency part of the image. (C) 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

Work efficiency of unmanned construction is 40% lower than that of on-board operation because of lack of visual information. Despite previous researches to acquire external views using drones and image processing, finding out optimum and allowable camera placements on actual construction machinery for chosen scenario and machine has been difficult. This work therefore documents the derivation of optimum and allowable camera placements with skilled subjects and using an actual construction machinery for digging and releasing tasks. The experimental results indicate an optimum pan angle of 90 degrees, allowable pan angles of 60 degrees-90 degrees and allowable tilt angles of > 45 degrees during digging, and allowable pan angles of 45 degrees-135 degrees, an optimum tilt angle of 90 degrees, and al-lowable tilt angles of 60 degrees-90 degrees during releasing. These results can be referred to the camera placements for unmanned construction sites to improve work efficiency. We will derive the camera placements for other scenarios.

With the increasing use of backpacks in recent years, proper reduction of shoulder load has become essential. We focused on nociceptive pain, which is a physiological warning signal, and performed subjective evaluation as an index to determine appropriate loading. In this paper, we investigated the relationship between a single-point pressure stimulus on the shoulder and pain and characterized pain sensitivity for each region of the shoulder. In the experiment, seven subjects rated their pain level upon stimulation using a pain scale. In data analysis, the rating scores were revised to an equivalence scale, and the relation between the pain score and stimulus intensity was approximated by a sigmoid function. Moreover, pain thresholds were estimated by approximate expression and classified into four sensitivity levels after normalizing. Combining the measurement points, we proposed characterization mapping of pain sensitivity and showed the pain sensitivity level at each measurement point. The results of pain rating for the strongest stimulus in the experiment, characterization mapping, and the results of pain sensitivity at each measurement point were anatomically matched. As such, we clarified the shoulder regions that can actively support load and those that should avoid loading. These results can be used to evaluate and improve backpack strap design.

Future robots are expected to share their workspace with humans. Controlling and limiting the forces that such robots exert on their environment is crucial. While force control can be achieved actively with the help of force sensing, passive mechanisms have no time delay in their response to external forces, and would therefore be preferable. Series clutch actuators can be used to achieve high levels of safety and backdrivability. This work presents the first implementation of a linear series clutch actuator. It can exert forces of more than 110N while weighing less than 2kg. Force controllability and safety are demonstrated. Static balancing, which is important for the application in a vertical joint, is also implemented. The power consumption is evaluated, and for a payload of 3kg and with the maximum speed of 94mm/s, the power consumed by the actuator is 11W. Overall, a practical implementation of a linear series clutch actuator is reported, which can be used for future collaborative robots.

We consider the design of under-actuated articulated mechanism that are able to maintain stable static balance. Our method augments an user-provided design with counter-weights whose mass and attachment locations are automatically computed. The optimized counterweights adjust the center of gravity such that, for bounded external perturbations, the mechanism returns to its original configuration.Using our sketch-based system, we present several examples illustrating a wide range of user-provided designs can be successfully converted into statically-balanced mechanisms. We further validate our results with a set of physical prototypes.

Measuring the forces that humans exert with their fingers could have many potential applications, such as skill transfer from human experts to robots or monitoring humans. In this paper we introduce the "Exo-Glove"system, which can measure the joint angles and forces acting on the human finger without covering the skin that is in contact with the manipulated object. In particular, 3-axis sensors measure the deformation of the human skin on the sides of the finger to indirectly measure the 3-axis forces acting on the finger. To provide a frame of reference for the sensors, and to measure the joint angles of the human finger, an exoskeleton with remote center of motion (RCM) joints is used. Experiments showed that with the exoskeleton the quality of the force measurements can be improved.

Adaptive assistance of gait training robots has been determined to improve gait performance through motion assistance. An important control role during walking is to avoid tripping by controlling minimum toe clearance (MTC), which is an indicator of tripping risk, to avoid its decrease among gait cycles. No conventional gait training robots can adjust assistance timing based on MTC. In this paper, we propose a system that applies force intermittently based on the MTC prediction algorithm to encourage people to avoid lowering the MTC. This prediction algorithm is based on a radial basis function network, the input data of which include the angles, angular velocities, and angular accelerations of the hip, knee, and ankle joints in the sagittal and coronal planes at toe-off. The cable-driven system that can switch between assistance and non-assistance modes applies force when the predicted MTC is lower than the mean value. Nine participants were asked to walk on a treadmill, and we tested the effect of the system. The MTC data before, during, and after the assistance phase were analyzed for 120 s. The results showed that the minimum and first quartile values of MTC could be increased after the assistance phase.

The detection of gait events with wearable sensors is necessary for a robotic system interacting with walking people. Conventional gait phase detection methods are based on machine learning. However, this method cannot detect a gait event every gait cycle because it is difficult to extract characteristic points. Additionally, using only angular information for detection is beneficial because angular information is needed for the control and evaluation of the robots. This paper proposes a novel algorithm for the detection of heel contact and toe-off using the inter-joint coordination of the hip, knee, and ankle joints that has a lower-dimensional structure. The proposed algorithm derives the four planes in the angular space and finds the switching points of the planes. Seven participants walked on force plates that measured the force of the foot against the floor. The error was less than 0.035 s when the gait events were detected after calculating planes using the first gait datum. The change in the patterns of the inter-joint coordination reflected the change in gait phases. Although the data were calculated offline, the results show that the heel contact and toe-off could be detected as soon as the angles were sensed once the planes were derived.

The detection of gait events with wearable sensors is necessary for a robotic system interacting with walking people. Conventional gait phase detection methods are based on machine learning. However, this method cannot detect a gait event every gait cycle because it is difficult to extract characteristic points. Additionally, using only angular information for detection is beneficial because angular information is needed for the control and evaluation of the robots. This paper proposes a novel algorithm for the detection of heel contact and toe-off using the inter-joint coordination of the hip, knee, and ankle joints that has a lower-dimensional structure. The proposed algorithm derives the four planes in the angular space and finds the switching points of the planes. Seven participants walked on force plates that measured the force of the foot against the floor. The error was less than 0.035 s when the gait events were detected after calculating planes using the first gait datum. The change in the patterns of the inter-joint coordination reflected the change in gait phases. Although the data were calculated offline, the results show that the heel contact and toe-off could be detected as soon as the angles were sensed once the planes were derived.

One of the most important problems in teleoperation of heavy machinery is that the work efficiency of teleoperation is lower than half that of a typical boarding operation. This difference is primarily caused by operators' difficulty in creating mental representations (i.e., cognitive maps) of work sites. Operators have two opportunities to acquire information, namely before work and during work, because the introduction of teleoperation requires about one week. Therefore, we have developed a view system to be used before work to provide environmental information concerning work sites on the basis of human spatial cognition. Cognitive maps can be built by acquiring knowledge from two perspectives-the survey perspective and the route perspective. We display an external view from any viewpoint to acquire knowledge from a survey perspective and a view from an operator's viewpoint, which can be modified by the operator's intention to acquire knowledge from the route perspective. Experimental results using a simulator suggested that a proposed view system could help operators acquire cognitive maps, which may lead to a decrease in task time, the number of stops, and the moving distance and an increase in speed during grasping.

This paper presents major improvements to a multimodal, adjustable sensitivity skin sensor module. It employs a geomagnetic 3-axis Hall effect sensor to measure changes in the position of a magnetic field generated by an electromagnet. The electromagnet is mounted on a flexible material, and different current values can be supplied to it, enabling adjustments to the sensitivity of the sensor during operation. Capacitive sensing has been added in this iteration of the module, with two sensing modalities: "pre-touch" detection with proximity sensing and normal force capacitive sensing. The sensor has been designed to be interconnected with other sensor modules to be able to cover large surfaces of a robot with normal and shear force sensing and object proximity detection. Furthermore, this paper introduces important size reductions of the previous sensor design, calibration results, and further analysis of other sensor characteristics.

We previously developed a four-arm, four-flipper disaster response robot called OCTOPUS to perform complex tasks at severe disaster sites. Its 22 degrees of freedom (DOF) enable advanced mobility and workability, but requires a minimum of two operators to control it. Considering the limitations of the current automated systems and the need to reduce human resources, it is practical to control OCTOPUS through a shared control. This entails a human-automation (HA) control system that independently allocates control authority between a human and automated system so as to simultaneously cover all DOF in what we call 'separative shared control'. To establish an HA control system, control authority allocation (CAA) must be carefully designed. We first performed experiments with a human-human (HH) control system using a VR simulator. We determined four tasks and five CAAs and then selected the optimum CAA by analyzing the results. We then developed an HA control system utilizing the selected CAA that replaces either human operator with a simple automated system. We compared the performance of the HH and HA control systems in the above four tasks using the VR simulator and found that the HA achieved less workload and higher subjective usability than the HH.

The sensitivity and measuring range of sensors are usually characteristics set before the production stage. However, sensors which are used as part of skin for robots would benefit from the ability to actively adjust their sensitivity during operation. Adjustable sensitivity implicitly broadens the range of operation of the sensor, which in turn is beneficial for interacting with both light and heavy objects. In this letter, we present the second iteration of a prototype sensor module that can be used as a skin sensor for robots. The sensor uses a flat electromagnet above a 3-axis magnetic sensor, separated by a layer of flexible material. The sensitivity of the sensor can be adjusted by changing the current that flows through the flat electromagnet. Each sensor module includes a microcontroller, providing digital output, multimodal and distributed sensing capabilities. Enhancements on the previous module version are discussed and implemented. Furthermore, we present results on temperature and magnetic field strength of the new coil board, and experiments on two possible different flexible materials to be used as a middle layer.

Tactile feedback is an important sensory information that contributes to our ability to perform various manipulation tasks. Soft sensor skin aims at providing, e.g., anthropomorphic robot hands with the sense of touch to resemble this ability in teleoperation or prosthetic applications. However, the exploration of an objects texture using a robot hand involves various dynamic contact scenarios. Depending on the relative dynamics between the soft sensor skin and the object, the difficulty and the dimensionality of the recognition task changes dynamically during the manipulation of an object. We deployed a deep gated recurrent unit (GRU)-ensemble with unweighted averaging that allows the texture recognition algorithm to dynamically scale with the number of contact points during active tactile texture exploration while maintaining a high accuracy. We experimentally verify the approach by evaluating the prediction performance of the GRU-ensemble on the data that was gathered during the active tactile exploration of four objects of daily living by means of a uSkin sensor module providing up to 16 3-axis force vectors at 100Hz sampling frequency. The accuracy of 100% suggests that deep GRU-ensembles offer a scalable option for reliable texture recognition in active tactile exploration for the implementation into tactile feedback systems.

For providing services where environments exist multiple humans or obstacles, robots need to move with considering multiple humans simultaneously, react quickly to the behavioral changes of pedestrians, choose acceleration/deceleration appropriately, and predict human’s behavioral changes. We thus propose a multiple moving obstacles avoiding method, called Iterative Dynamic Waypoint Navigation (IDWN). The method consists of (i) interference estimation, to predict future interference with a pedestrian, (ii) cluster estimation, to avoid pedestrians as a cluster who cannot be avoided without entering their personal space, (iii) path generator, to calculate waypoints of default speed, acceleration and deceleration paths, (iv) iterative search, to repeat the above three functions for all pedestrians in the environment, and (v) cost calculation, to select the lowest cost trajectory. For reducing the calculation cost, some heuristics, omitting candidates which theoretically increase the cost, are introduced. We implemented the method into a simulator and conducted evaluation experiment by comparing with several conventional methods. As the result of the experiment, we verified that IDWN worked well in versatile dynamic environments with multiple moving obstacles with less computation time.

The number of medical-condition-caused car accidents (MCCCAs) in transport industry (bus, truck, and taxi) recently increases. MCCCAs including cerebrovascular and cardiovascular disease lead to loss of consciousness, thus result in injury and loss of life, and heavy compensation payment. Toward this problem, conventional systems detect closing of eyes, and fallen down state as to prevent car collisions. However, the support is taken after driver losing consciousness. To prevent MCCCAs, it is important to find out abnormal signs before driver losing consciousness. It is challenging to detect abnormal signs not only early but also with high confidence level (CL). This paper proposes a novel method that multi-modally monitors driver to detect abnormal signs which can be cues for estimating a driving-disable state in future and performs voice interaction based on the result of monitoring to clarify the internal state of the driver. Considering no data of abnormal signs, this study developed the system using normal data and pseudo abnormal data, and method of outlier detection was used for abnormal signs detection. As results of experiment, we found the relationship between cue signs and CL, and the proposed system can detect 'sleepiness' state with accuracy of 80%. Voice interaction system did not increase driver's mental demand.

Transmission systems that enable speed, torque, and direction conversion with high responsiveness and backdrivability are strongly required for higher performance robotic systems. Engagement states can be changed by clutches, but traditional clutches cannot change the direction and provide sufficient backdrivability. On the other hand, direction conversion can be realized by gear box such as bevel gears, but its backdrivability is poor. Thus, we newly develop a prototype transmission system with backdrivablity and responsiveness integrating clutch and direction converter using magnetorheological fluid (MRF). MRF is a functional fluid consisted of magnetic particles and carrier fluids which can change its viscosity rapidly and continuously according to the strength of magnetic field. MRF clutch consists of driving and driven shaft connected by vanes and coils for controlling MRF. MRF direction converter consists of bevel gears, brake, and MRF for reversing the output direction. In addition to traditional functions, such as speed and torque conversion, the proposed power transmission system provides five working modes: forward direction, reverse direction, and three kinds of free, according to states of the MRF clutch, MRF direction converter, and brake. Preliminary experiments revealed that the proposed transmission system could adequately realize implemented functions.

Distracted driving is one of the most common causes of traffic accidents around the world. Recognizing the driver's gaze direction during a maneuver could be an essential step for avoiding the matter mentioned above. Thus, we propose a gaze zone classification system that serves as a base of supporting systems for driver's situation awareness. However, the challenge is to estimate the driver's gaze inside not ideal scenarios, specifically in this work, scenarios where may occur self-occlusions or non-aligned head and eyes direction of the driver. Firstly, towards solving miss classifications during self-occlusions scenarios, we designed a novel protocol where a 3D full facial geometry reconstruction of the driver from a single 2D image is made using the state-of-the-art method PRNet. To solve the miss classification when the driver's head and eyes direction are not aligned, eyes and head information are extracted. After this, based on a mix of different data pre-processing and deep learning methods, we achieved a robust classifier in situations where self-occlusions or non-aligned head and eyes direction of the driver occur. Our results from the experiments explicitly measure and show that the proposed method can make an accurate classification for the two before-mentioned problems. Moreover, we demonstrate that our model generalizes new drivers while being a portable and extensible system, making it easy-adaptable for various automobiles.

In this paper, we present a suction cup by applying magnetorheological elastomers (MREs) that can be used for the wall climbing robot and picking application. MRE is a smart material in which the ferromagnetic particles are dispersed in the elastomer. In conventional research, MREs are often used as a vibration absorber, since the physical or mechanical properties can be altered upon the application of a magnetic field. In this paper, we focus on the expansion of the use of MRE material. When the magnetic field is added, the MRE will be attracted to the magnetic generator due to the ferromagnetic particles inside. The basic mathematical model between magnetic force and elasticity is discussed by us, which supports the working principle of building MRE suction cup. The MRE suction cup contains a permanent magnetic field generator unit and a MRE membrane unit. A servo motor is used to control the ON/OFF of the magnetic generator, so that the MRE membrane can be activated and deactivated. In this prototype, we focus on picking objects with flat surfaces as the first attempt. The proposed suction cup can achieve appropriate suction force, low noise, energy-saving. In this paper, the operation method of MREs, the design of the suction cup, and the evaluation of the prototype are conducted.

The maintenance of water pipelines is quite essential since the leakage, deformation, and damage can bring fatal problems to the stable supply and safe use of water resource. In our former study, the transmission approach of information (image, sensor data, etc.) from water pipe based on visible light relay communication technology has been proposed since the visible light has less signal attenuation, stronger anti-electromagnetic interference capability, and smaller bit error ratio (BER) than traditional wireless solutions in the water pipe. However, each inspection sensor in the pipe is still powered by the battery, and thus, the operation time of the sensor is severely restricted by the battery consumption. Solar panel has been introduced in the visible light communication (VLC) technology due to its large signal receiving area and higher efficiency than traditional photodiode (PD) receivers. In this study, the performance of the solar panel receiver for VLC and energy harvesting (EH) has been analyzed. Since the LED owns low irradiation energy for energy harvesting, the received power of solar panel is low. To solve these problems, a solution by using sunlight for EH and LED light for VLC was proposed. We performed several experiments to evaluate the energy harvesting based on current and voltage, and VLC quality based on waveform and BER. The results indicated that the hybrid transmission method could increase the receiving current of solar panel with less influence on the VLC quality.

Robot grippers and hands are commonly used to grasp various objects. However, for multi-link fingers, it is challenging to cover the joints with tactile sensors, which limits the safety and sensitivity of such fingers. In the current paper we use a remote center of motion (RCM) mechanism for the joints, which enables us to cover also the joints completely with soft and thick tactile sensors, in particular distributed 3-axis sensors. The RCM joints are implemented as 6-bars, and we evaluate their robustness in simulation. A real implementation of the gripper is tested by grasping various objects, and the resulting tactile sensor readings are presented.

In this letter, we present a new iteration of a prototype sensor module that can be used as a skin sensor for robots. The sensor uses a hybrid arrangement that consists in a small permanent magnet and an electromagnet working together above a 3-axis magnetic sensor. Both layers are separated by a flexible material that provides compliance to the sensor module. The sensitivity of the sensor can be adjusted by changing the flow of current on the electromagnet, while the permanent magnet increases the intensity of the overall magnetic field to optimize the utilization of the bandwidth of the sensor. Each sensor module includes a microcontroller with digital output that offers multimodal and distributed sensing capabilities. Problem areas of the previous version are indicated, the magnet array idea is presented and tested, and finally experiments are performed on different shapes of a flexible material to be used as middle layer.

Human experts have mastered many manual skills. Transferring those skills to robots or human novices is challenging. Not only the movements, but also the exerted forces are of importance. In this paper we propose a sensor that can measure the force vector acting on the human fingertip, without covering the part of the fingertip that is contact with the touched object. Compared to our previous iterations of this sensor, we use a spring instead of viscoelastic material as the deformable material for the sensor. While it is challenging to include a spring in the given form factor, springs have the benefit of lower hysteresis than viscoelastic materials. This paper presents the integration of the spring in the sensor and shows experimental validation with 8 subjects. Compared to our previous version of the sensor with 2 Hall effect sensors, we achieved improved sensing characteristics.

Work efficiency in unmanned construction is about 40% less than that in boarding operation owing to a lack of visual information. Thus, previous studies have developed methods to display rich visual information by adding multiple views. However, providing excess visual information can cause 'cognitive tunneling' in operators, which makes operators subconsciously attend to a limited view and ignore others. Therefore, we developed a cognitive untunneling multi-view system that enables operators to voluntarily select appropriate views depending on situations. The experimental results using a scale model indicate that the proposed view system could decrease work time, stoppage time and help operators switch views frequently and release their attention from a cab view compared with a conventional view system. These results suggest that the proposed view system can increase work efficiency and help operators avoid cognitive tunneling. In the future, we will develop a system to identify situations and change views automatically.

Due to functional limitations in certain situations, the driver receives a request to intervene from automated vehicles operating level 3. Unscheduled intervention of control authority would lead to insufficient situational awareness, then this will make dangerous situations. The purpose of this study is thus to propose tactical-level input (TLI) method with a multimodal driver-vehicle interface (DVI) for the human-centered intervention. The proposed DVI system includes touchscreen, hand-gesture, and haptic interfaces that enable interaction between driver and vehicle, and TLI along with such DVI system can enhance situational awareness. We performed unscheduled takeover experiments using a driving simulator to evaluate the proposed intervention system. The experimental results indicate that TLI can reduce reaction time and driver workload, and moreover, most drivers preferred the use of TLI than manual takeover.

The regular inspection of underground facilities such as pipelines is absolutely essential. Pipeline leakage caused by corrosion and deformation must be detected in time, otherwise, it may cause fatal disasters for human beings. In our previous research, a robot chain system (RCS) based on visible light relay communication (VLRC) for pipe inspection has been developed. This system can basically realize the light-based transmission of control command signals and illuminance-based coordinated movement, whereas the collection and transmission approach of the pipe leakage image have not been studied. Compared with former in-pipe wireless communication techniques, VLRC can not only overcome the instability and inefficiency of in-pipe data transmission but also extend the communication range with high transmission rates. The most important feature is that it can provide a stable illumination and high-quality communication for pipe inspection robot and finally improve the energy efficiency. Hence, the aim of this article is to analyze the performance of VLRC-based image transmission in the pipe and in the future provide a high-quality, long-range, and high-efficiency image transmission for complex infrastructure inspection with RCS. The transmission systems based on two signal transmission modes analog image signal relay transmission (AISRT) and digital image frame relay transmission (DIFRT) have been proposed. Multiple experiments including the waveform test, the test of transmission features with different bit error rate (BER), and in the different mediums were conducted between these two systems. The experiment revealed that DIFRT was superior to the AISRT in terms of the relatively high-quality image transmission and reconstruction quality. It could better overcome the attenuation brought by the absorption and scattering effects and finally increased the transmission range than former communication methods. The DIFRT system could also operate at 50 kbps with relatively low BER whether in the air or water. The technique in this research could potentially provide a new strategy for implementations in the stable, effective, high-speed, and long-range image transmission of the robots in some other special environments such as tunnel, mine, and underwater, etc.

The adaptive control of gait training robots is aimed at improving the gait performance by assisting motion. In conventional robotics, it has not been possible to adjust the robotic parameters by predicting the toe motion, which is considered a tripping risk indicator. The prediction of toe clearance during walking can decrease the risk of tripping. In this paper, we propose a novel method of predicting toe clearance that uses a radial basis function network. The input data were the angles, angular velocities, and angular accelerations of the hip, knee, and ankle joints in the sagittal plane at the beginning of the swing phase. In the experiments, seven subjects walked on a treadmill for 360 s. The radial basis function network was trained with gait data ranging from 20 to 200 data points and tested with 100 data points. The root mean square error between the true and predicted values was 3.28 mm for the maximum toe clearance in the earlier swing phase and 2.30 mm for the minimum toe clearance in the later swing phase. Moreover, using gait data of other five subjects, the root mean square error between the true and predicted values was 4.04 mm for the maximum toe clearance and 2.88 mm for the minimum toe clearance when the walking velocity changed. This provided higher prediction accuracy compared with existing methods. The proposed algorithm used the information of joint movements at the start of the swing phase and could predict both the future maximum and minimum toe clearances within the same swing phase.

Magnetorheological Fluid (MRF) is a functional fluid in which apparent viscosity changes quickly, continuously and reversibly according to the strength of the magnetic field applied. On the basis of the peculiar features of MRFs various studies have been conducted mainly on functional fluids in the power fluid field. MRF consists of magnetic substances of the size 1-10 mu m which are dispersed in lubrication oil having low viscosity and specific gravity. There is no MRF which can be used in robots for precise control because it cannot guarantee the reliability. We here aim to acquire advanced robot mechanism by fusing material-mechanical technology and crossing hypothesis-experiment cycle. As a result, we found that anti-sedimentation of MRF can be regarded as the most important features for implementation in robotic mechanisms. In this paper, the anti-sedimentation MRF is manufactured and tested in a MRF damper. We first obtained a common method to manufacture the anti-sedimentation MR fluid, by designing the medium, the magnetic substance, and the additive. In the sedimentation test, the proposed MR fluid had the minimum separation compared to the commercial product. Secondly, in experiment using the MR damper, the anti-sedimentation MRF had superior stability compared to the commercial product.

Human-aware navigation is an essential requirement for autonomous robots in human-coexisting environments. The goal of conventional navigation is to find a path for a robot to pass through safely and efficiently without colliding with human. Note that if such a path cannot be found, the robot stops until a path is clear. Thus, such collision-avoidance based passive navigation does not work in a congested or narrow space. To avoid this freezing problem, the robot should induce humans to make a space for passing by an adequate inducement method, such as body movement, speech, and touch, depending on the situation. A robot that deliberately clears a path with such actions may make humans uncomfortable, so the robot should also utilize inducements to avoid causing negative feelings. In this study, we propose a fundamental framework of interactive navigation with situation-adaptive multimodal inducement. For a preliminary study, we target a passing scenario in a narrow corridor where two humans are standing and adopt a model-based approach focusing on common parameters. The suitable inducement basically varies depending on the largest space through which a robot can pass, distance between the robot and a human, and human behavior such as conversing. We thus develop a situation-adaptive inducement selector on the basis of the relationship between human-robot proximity and allowable inducement strength, considering robot efficiency and human psychology. The proposed interactive navigation system was tested across some contextual scenarios and compared with a fundamental path planner. The experimental results indicated that the proposed system solved freezing problems, provided a safe and efficient trajectory, and improved humans' psychological reaction although the evidence was limited to robot planner and hardware design we used as well as certain scenes, contexts, and participants.

The gas pipeline requires regular inspection since the leakage brings damage to the stable gas supply. Compared to current detection methods such as destructive inspection, using pipeline robots has advantages including low cost and high efficiency. However, they have a limited inspection range in the complex pipe owing to restrictions by the cable friction or wireless signal attenuation. In our former study, to extend the inspection range, we proposed a robot chain system based on wireless relay communication (WRC). However, some drawbacks still remain such as imprecision of evaluation based on received signal strength indication (RSSI), large data error ratio, and loss of signals. In this article, we thus propose a new approach based on visible light relay communication (VLRC) and illuminance assessment. This method enables robots to communicate by the light signal relay', which has advantages in good communication quality, less attenuation, and high precision in the pipe. To ensure the stability of VLRC, the illuminance-based evaluation method is adopted due to higher stability than the wireless-based approach. As a preliminary evaluation, several tests about signal waveform, communication quality, and coordinated movement were conducted. The results indicate that the proposed system can extend the inspection range with less data error ratio and more stable communication.

Although conventional service providers are independent from each other when attending most of the population, demanded services are changing along with the social structure. Especially in the case of Taiwan, the number of co-working families has been increasing, and self-employed households occupy a large proportion of all working forms. Due to their diverse lifestyles and work styles, services that are suitable for personal objectives and that optimize the use of time are required. To meet this demand, it is important to connect people and city facilities to make it easier to provide suitable services. Based on those backgrounds, an innovated personal service platform in Taiwan is proposed, focusing on three factors, including time, place and personal information to connect people and city service facilities. Among various kinds of services, we targets services purchased in cities such as sales, mobility services, health services, government services and so on. It aims to link these services flexibly and dynamically to achieve personal objectives according to each situation. And, it can provide suitable services for a variety of every-day living situations. With this system, people can increase satisfaction and free time, improving life quality while making the economy more dynamic.

© 2019 IEEE. Recent driving assistance technologies such as Electronic Stability Control (ESC) and auto brake system release drivers from complicated driving tasks. On the other hand, there is concern that it reduces pleasure feelings of a driver if these system's behaviors are different from the driver's intention. To avoid such problem, it is important to evaluate the driver's intention and decision-making process, and design the assistance system to fit it. In this research, we propose an unsupervised reinforcement learning driver model based on human cognitive mechanism and human brain architecture. Because this study's objective is to analyze the process of driving decision making, we hire a simple actor-critic model as a driver model. We set learning parameters from the driver's decision making characteristics which are derived from the task execution process of the human brain, and set state space from driver's sensory characteristics. This driver model can predict lane change decision making adequately and shows high accuracy (ACC=94%) on verification tests with real driving data. This result is similar to unpublished results of a deep neural network driver model which use the same data as teaching data. From these results, we consider that the proposed reward function and learned state space represent the driver's decision making characteristics.

Gait assistance robots are used to improve gait performance ability or perform gait motion with an assistance for several articular motions. The sparing use of a gait assistance robot to decrease the duration of the robot's assistance is important for keeping the ability to perform a movement when the robot assists walking. In previous research, methods of ensuring a compliance mechanism and control method have been studied, and assistance for articular motions has been conducted independently using actuators corresponding to each articular motion. In this paper, we propose a wire-driven gait assistance robot to aid both hip and knee articular flexion motions by applying just one force to assist motion in the swing phase. We focused on a force that assists hip and knee flexion motion, and designed a robot with a compensation mechanism for the wire length. We used an assistance timing detection method for the robot, conducting tensile force control based on information from the hip, knee, and ankle angles. We carried out an experiment to investigate the controlled performance of the proposed robot and the effect on hip and knee angular velocity. We confirmed that the proposed robotic system can aid both hip and knee articular motion with just one force application.

© 2018 IEEE. The advance of driving assistance technologies such as Electronic Stability Control (ESC) or auto break system, drivers are released from complicated driving tasks. On the other hand, there is concern that it reduces pleasure feelings of a driver if these system&#039;s behaviors are different from the driver&#039;s intention. To avoid such problem, it is important to evaluate the driver&#039;s intention and decision-making process, and design the assistance system to fit it. Although methods such as sensory subjective evaluation are commonly used, the human cognitive mechanism design behind them is not yet fully understood. In this paper, we introduce a novel method for evaluating driver&#039;s decision-making process based on the numerical simulation of the driver&#039;s behavior. By using this method, the assistance system can substitute the driver appropriately and driver can accept the system&#039;s maneuver because which is same as the driver&#039;s intention. As an example of this method we evaluate the relationship between decision-making timing and estimation time length of the driver&#039;s model. One possible method to simulate the driver&#039;s decision-making is machine learning. Reinforcement learning has been studied for simulating the human&#039;s brain function to learn and decide as action and state model. We used machine learning to create the reinforcement learning driver model, and a simple vehicle simulation model which are combined as a human-vehicle model. We used the simple vehicle and driver model because the aim of this research is to investigate whether the driver&#039;s decision-making process can be simulated or not. Then the model is simulated to learn to drive on a highway with 3 lanes and other vehicles. The simulated driver made some single lane change to pass a slower vehicle in front or to go out from highway at an interchange. Results showed that the decision-making timing depend on the estimation time of the reinforcement learning model. We exposed that the model behaves similar to general driver&#039;s behavior when the estimation time was settled as 7sec which is derived from human brain&#039;s cognitive mechanism. In conclusion, our simulation model based on human cognitive mechanism can simulate the driver&#039;s lane change decision-making behavior adequately.

Gait assistance robots are used to improve gait performance ability or perform gait motion with an assistance for several articular motions. The sparing use of a gait assistance robot to decrease the duration of the robot’s assistance is important for keeping the ability to perform a movement when the robot assists walking. In previous research, methods of ensuring a compliance mechanism and control method have been studied, and assistance for articular motions has been conducted independently using actuators corresponding to each articular motion. In this paper, we propose a wire-driven gait assistance robot to aid both hip and knee articular flexion motions by applying just one force to assist motion in the swing phase. We focused on a force that assists hip and knee flexion motion, and designed a robot with a compensation mechanism for the wire length. We used an assistance timing detection method for the robot, conducting tensile force control based on information from the hip, knee, and ankle angles. We carried out an experiment to investigate the controlled performance of the proposed robot and the effect on hip and knee angular velocity. We confirmed that the proposed robotic system can aid both hip and knee articular motion with just one force application.

The adaptive control of gait training robots is aimed at improving the gait performance by assisting motion. In conventional robotics, it has not been possible to adjust the robotic parameters by predicting the toe motion, which is considered a tripping risk indicator. The prediction of toe clearance during walking can decrease the risk of tripping. In this paper, we propose a novel method of predicting toe clearance that uses a radial basis function network. The input data were the angles, angular velocities, and angular accelerations of the hip, knee, and ankle joints in the sagittal plane at the beginning of the swing phase. In the experiments, seven subjects walked on a treadmill for 360 s. The radial basis function network was trained with gait data ranging from 20 to 200 data points and tested with 100 data points. The root mean square error between the true and predicted values was 3.28 mm for the maximum toe clearance in the earlier swing phase and 2.30 mm for the minimum toe clearance in the later swing phase. Moreover, using gait data of other five subjects, the root mean square error between the true and predicted values was 4.04 mm for the maximum toe clearance and 2.88 mm for the minimum toe clearance when the walking velocity changed. This provided higher prediction accuracy compared with existing methods. The proposed algorithm used the information of joint movements at the start of the swing phase and could predict both the future maximum and minimum toe clearances within the same swing phase.

Although conventional service providers are independent from each other when attending most of the population, demanded services are changing along with the social structure. Especially in the case of Taiwan, the number of co-working families has been increasing, and self-employed households occupy a large proportion of all working forms. Due to their diverse lifestyles and work styles, services that are suitable for personal objectives and that optimize the use of time are required. To meet this demand, it is important to connect people and city facilities to make it easier to provide suitable services. Based on those backgrounds, an innovated personal service platform in Taiwan is proposed, focusing on three factors, including time, place and personal information to connect people and city service facilities. Among various kinds of services, we targets services purchased in cities such as sales, mobility services, health services, government services and so on. It aims to link these services flexibly and dynamically to achieve personal objectives according to each situation. And, it can provide suitable services for a variety of every-day living situations. With this system, people can increase satisfaction and free time, improving life quality while making the economy more dynamic.

© 2019 Design Society. All rights reserved. Pipe inspection robots have been developed to reduce the cost and time required for gas pipe inspection. However, these robots have been developed using a scrap and build method and are not used in practice. In this paper, we propose a method of virtual pipe inspection simulation to clarify the parameters that are important in increasing the robot's ease of use. This paper presents the results obtained by a feasibility study with regard to pipe simulation. We developed a virtual pipe by simulating eight actual turns of an external gas pipe, and a robot equipped with camera at the tip. In the experiments, three individuals working in the field of gas inspection carried out the operation. We obtained questionnaire, time, and brain activity data. The results revealed various important points that must be considered in practical simulation and robot design. In conclusion, the virtual pipe simulation can be useful in developing the design of a pipe inspection robot.

Pipeline inspection robots are recently expected to be deployed to pipelines with longer distance and more complex configuration. These robots require high performance in wireless communication among robots and outside. The radio frequency or infrared radiation frequency can be one of the promising wireless communication method, but they would have drawbacks in pipeline environments, such as electromagnetic interference and low energy efficiency. In this study, we focus on visible light communication (VLC), which has a high transmission rate, strong anti-interference capability, and large bandwidth, compared with the above methods. VLC also has an interesting feature that can simultaneously realize data communication and illuminating dark pipeline environment for pipe robot. As a preliminary study of VLC application, we developed a control system based on VLC for a wheeled robot, which includes a VLC transmitter and receiver. The transmitter and receiver can encode and decode the light signals. Finally, we tested the signal waveform of transmitter and receiver, and evaluate the performance of this system in real pipeline robot. The experimental results revealed that the proposed VLC system could achieve a reliable signal transmission and provide well control and illumination for the robot in the pipe.

Herein, unmanned construction technology that allows remote control of construction machinery has been introduced for post-disaster recovery efforts. One of the most significant problems in unmanned construction is that work efficiency is less than half of that in manned operations owing to the lack of visual information. Thus, deriving optimum and allowable external sideway views can lead to higher efficiencies. In this study, we investigate optimum and allowable pan and tilt angles of external views for grasping and placing tasks as these tasks are required in disaster sites. Humans primarily recognize objects from canonical views with the least amount of occlusions, and with an allowable rotation of 30 degrees. Thus, we hypothesize that external views with pan and tilt angles of 90 degrees could be canonical views for grasping and placing; the allowable range of both pan and tilt angles was +/- 30 degrees. We conducted experiments using a 1/20 scale model to derive an optimum and allowable range of pan and tilt angles. The experimental results suggest that the optimum pan angle is 90 degrees, and the allowable range for grasping is +/- 30 degrees; the range for placing was from 30 degrees to 135 degrees. In addition, the results indicate that the optimum tilt angle is 60 degrees, and the allowable range is +/- 30 degrees.

How to improve task performance and how to control a robot in extreme environments when just a few sensors can be used to obtain environmental information are two of the problems for disaster response robots (DRRs). Compared with conventional DRRs, multi-arm multi-flipper crawler type robot (MAMFR) have high mobility and task-execution capabilities. Because, crawler robots and quadruped robots have complementary advantages in locomotion, therefore we have the vision to combine both of these advantages in MAMFR. Usually, MAMFR (like four-arm four-flipper robot OCTOPUS) was designed for working in extreme environments such as that with heavy smoke and fog. Therefore it is a quite necessary requirement that DRR should have the ability to work in the situation even if vision and laser sensors are not available. To maximize terrains adaption ability, self-balancing capability, and obstacle getting over capability in unstructured disaster site, as well as reduce the difficulty of robot control, we proposed a semi-autonomous control system to realize this compound locomotion method for MAMFRs. In this control strategy, robot can explore the terrain and obtain basic information about the surrounding by its structure and internal sensors, such as encoder and inertial measurement unit. Except that control system also can recognize the relative positional relationship between robot and surrounding environment through its arms and crawlers state when robot moving. Because the control rules is simple but effective, and each part can adjust its own state automatically according to robot state and explored terrain, MRMFRs have better terrain adaptability and stability. Experimental results with a virtual reality simulator indicated that the designed control system significantly improved stability and mobility of robot in tasks, it also indicated that robot can adapt complex terrain when controlled by designed control system.

© 2018 IEEE. In this paper, we propose a printed self-oscillatory mechanism inspired by an electric bell. We have proposed paper mechatronics to make a robot with printing methods. In the paper mechatronics, we also aim to fabricate a controller with printing methods. We attempt to obtain a control signal from a mechanism. An electric bell generates an oscillatory signal with a coupling mechanism between a coil and a magnet. We develop a printed electric bell on a paper with printing methods. We propose the design of the electric bell which can be fabricated on a sheet of paper. The coil and the wiring were printed on the paper, and the device was fabricated by folding the paper. We demonstrate that the device oscillates for 23 min by applying a 0.5 A of constant current. The design is scalable to be applied for microsystems with using printing methods.

Powered prostheses with low degree of freedom (DoF) have been developed for people with disabilities to assist daily tasks. These prostheses neglect the user's compensatory movements caused by the low degree of freedom. We assume that the movements can be reduced by well-designed controller of the devices. This paper explores an optimal control gain of the powered prosthesis to prevent the user from compensatory movements through experiments. In the experiments, we developed 1-DoF hand prosthesis with a position-controlled servo, which includes the constant gain as a feed-forward term. The compensatory movements are regarded as a joint torque at a shoulder (abduction/adduction). 4 intact subjects performed a pick-and-place task, using the prosthesis with several control gains. The empirical results show that there was the optimal gain for each subject, which reduces their compensatory movement.

Prediction of minimum toe clearance (MTC) during walking can decrease the risk of tripping. In this paper, we proposed a novel MTC prediction method using a radial basis function network. Input data were the angles, angular velocities, and angular accelerations of the hip, knee, and ankle joints in the sagittal plane at the start of the swing phase. In experiments, five subjects walked on a treadmill for 360 s. The radial basis function network was trained with 60 s of gait data and tested with the remaining 300 s of gait data. The root mean square error between the true and predicted MTC values was lower than 2.79 mm in all subjects.

Gas pipeline requires to be inspected regularly for leakages caused by natural disaster. Robots are widely used for pipeline inspection since they are more convenient than manual inspection. Several problems, however, exist due to the restriction by complex pipe networks. The most significant one is limited inspection range caused by restriction of cable length or wireless signal attenuation. In this paper, we proposed a concept of wireless relay communication to assist robot to extend the inspection range, and we newly developed a tracked robot chain system. In this system, each robot serves as a relay communication node. Leakage information of pipes are transmitted via these relay nodes. To ensure the stability of relay communication between adjacent robots, we adopted RSSI (received signal strength indication)-based evaluation method for cooperative and coordinated movement of robot chain system. Moreover, wireless application layer communication protocol (WALCP) was used to increase the stable performance of wireless relay communication. Each robot can self-navigate based on distance measurement module, which enables robots to pass through an elbow junction. Multiple experiments to evaluate relay transmission efficiency, RSSI-based cooperative movement, and comprehensive performance were conducted. Results revealed that our proposed system could realize relatively accurate relay transmission and RSSI-based coordinated movement.

Some electric-powered wheelchairs are recently redefined as personal mobility devices. Their users are not only elderly or handicapped people, but also passengers with large baggage or pedestrians going from station to destination, i.e., last-mile transport. Consequently, people with different operation skills and expectations on personal mobility would become new users of this kind of devices. Safe and comfort travel in human co-existing environment such as sidewalks and airports is a social expectation for personal mobility. In order to realize this, understanding the operation skill of each user by a practical and simple method is essential. This paper thus introduced a skill level classification method by machine learning using only joystick data as input. In order to determine the number of skill level clusters, basic 26 features of joystick operation data are used for unsupervised clustering (single-linkage). We then made evaluation indexes by using speed, speed control, and direction control. For a five-level classification by using gradient boosting as supervised learning, we achieved a 67% accuracy (tolerance: 0) and a 98% accuracy (tolerance: 1). Further analysis of the feature importance of gradient boosting revealed key features to a good operation. Results also show that skill level differed among people with different driving experiences.

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. We propose a simple methodology to paint carbon nanotube (CNT) powder with a soft brush onto an elastomer. A large deformation of dielectric elastomer actuator (DEA) occurs according to the small constraint of the electrodes. Uniform painting with a soft brush leads to a stable deformation, as demonstrated by the results of multiple trials. Unexpectedly, painting with a soft brush results in aligned materials on the elastomer. The oriented materials demonstrate anisotropic mechanical and electronic properties. This simple methodology should help realize innovative DEA applications.

Efficient navigation in a congested environment is immensely difficult for the current state-of-the-art mobile robots owing to the challenges related to both sensing and actuation. Consequently, conventional approaches in mobile robotics research prioritize safety by slowing down or stopping robot's movement under challenging circumstances. This paper considers an alternative approach in which the robot utilizes forearm contact to create space for itself and to act as a safety buffer, during very close or congested navigation interactions. First, two categories of contact methods are defined based on different navigation scenarios. These contact methods are analyzed through different contact positions and force directions in a set of comparative experiments. The results of the comparative experiments are then incorporated into a contact-based framework that outputs the necessity, and the appropriate form of forearm contact. Finally, a set of evaluation experiments are performed to test the usability and effectiveness of the constructed framework. The results indicate that the proposed framework effectively outputs appropriate contact according to the situation, and aids the robot while navigating through space-constrained scenarios.

Smooth and efficient robot navigation among humans is a crucial requirement for successful integration of robots in human society. Towards this end, an indispensable characteristic of robot action is legibility while communicating its intention. However, unlike humans, present robots cannot convey its intention through human-like non-verbal communication. This paper explores the use of projection indicators for communicating directional intent of a robot across three different 'crossing scenarios' as a means of overcoming the shortcomings of the robot's non-verbal communication abilities. The results of the study show statistically significant improvement in perceived feelings of the measured attributes when using the auxiliary communication method. The studied method also improves cooperation from the participants. Nevertheless, the improvement in perceived feeling does not necessarily replicate in terms of smoothness across all the scenarios.

© 2017 IEEE. Paper mechatronics has been proposed as a concept to fabricate robots with printing methods in our previous studies. Actuators fabricated by printing on a sheet material can be low-cost and lightweight. In this paper, we propose a swinging paper actuator driven by conduction Electrohydrodynamics (EHD). EHD is a phenomenon that generates flow by applying voltage to a dielectric liquid. The EHD flow is generated by the applied voltage in a wiring made by silver ink printed with an inkjet printer. The silver wiring was printed on a sheet of paper, and the paper with the wiring was folded manually to form its structure. We explored the possibility to manufacture EHD actuators by using the printing techniques developed in paper mechatronics. We expected to obtain high performance from the generated actuators due to the light weight of the paper printed structures. Finally, we succeeded in activating the swinging actuator by applying the high voltage to the paper electrode. We applied sine and square inputs, and investigated several characteristics. We found conditions to obtain the largest displacement in our situation. The swinging actuator proves a new concept to incorporate two alluring researches which are printed paper actuator and EHD phenomenon.

© 2017 IEEE. We developed a self-folding method of a printed paper. The paper self-folds along the printed line with a desktop inkjet printer to get 3D structure. In this paper, we introduce a method to fabricate multiple 3D structures simultaneously. Multiple development designs were arranged on A4 sized paper, and the A4 sized paper is printed to self-fold multiple developments. The proposed method has an advantage especially in small scale, because when design of the development is smaller, more numbers of structures can place in A4 sized paper and construct. Plenty of small and light 3D structures are fabricated simultaneously with low-cost. The feature has good affinity with swarm robotics, and many paper robots may locomote using their own structural intelligence obtained by the self-folding.

<p>Fall in stair is a serious problem. The misidentification of the stair height is one of the cause of fall. This paper presents an optical illusion induction system with augmented reality for support of ascending and descending stairs. The head-mounted display shows the vertical on the stair edge when ascending or horizontal line on the stair surface when descending. Human showed the AR system to perceive the stair higher when ascending or narrower when descending than actual. We constructed the model of the foot clearance by optical illusion based on the contrast sensitive function approximated with Gaussian function. In experiment, 10 participants climb and descend with AR system while we measured the foot trajectory. As a result, the coefficient of determination is over 0.7 when ascending, over 0.6 when descending in all participants. Furthermore, the toe height is significant higher during AR system than before in ascending (p<0.05). And, the horizontal toe distance is significant lower during AR system than before in descending (p<0.05). We concluded that the AR system make the participants perceive the optical illusion so the foot clearance increased enough.</p>

We propose an effective timing of intermittent force application by a gait assistance robot with a wire-driven system to increase the toe trajectory throughout the swing phase. We tested different timings of force application at the shank, employing the short-term assistance of the robot to increase toe clearance throughout the swing phase. The force was applied to the shank to generate knee flexion torque because the shank motion generated by the knee flexion motion makes the largest contribution to toe clearance. Four timings of the force application were considered: when starting knee flexion before toe-off, when lifting the foot, while maintaining knee flexion after toe-off, and when finishing knee flexion after toe-off. Furthermore, we evaluated changes in the toe trajectory and articular angles of the lower limb for each timing condition. We used a timing detection method for the robot conducting tensile force control based on information from the hip, knee, and ankle angles. For all participants, an increase in the knee flexion angle in the early swing phase due to the force application increased the toe clearance throughout the swing phase. We thus conclude that force application when the user begins lifting their toe is effective in increasing toe clearance throughout the swing phase.

The gas pipeline networks cover approximately 200,000, km in urban region in Japan. The risk caused by severe leakage, cracking, or corrosion will lead to a big accidence for human. Therefore, the gas pipeline robots for regular inspection and maintenance are very essential for the safe operation and supply of gas. In this paper, as a fundamental study of pipeline inspection robots, a new small wireless tracked robot for gas pipeline inspection and maintenance has been developed. Due to robot’s special kinematics, it is capable of moving in straight pipe, turn at elbow and also climb the inclined pipe flexibly and efficiently. Compared with previous robots, it owns two working modes: self-navigation (automated control) and manual control mode for pipeline inspection. Moreover, a preliminary experiment has been implemented with a typical M-pipeline in 10, cm diameter/4, m length. The experiment results revealed that self-navigation could improve the efficiency of inspection.

Four-arm four-flipper disaster response robot called OCTOPUS, which has 26 degrees of freedom (DOF), has been developed to engage in complex disaster response work. OCTOPUS adopts a multi-operator single-robot (MOSR) control system, and two operators manually control the robot. The pattern of control-authority allocation (CAA) for two operators largely affects performance of MOSR systems, so this study conducts usability tests in various CAAs, and derives a reasonable CAA pattern. So far, there are no uniform standard allocation rules for flippers and crawlers in multi-limb robots like OCTOPUS, therefore we specify five CAA patterns for investigation. Three fundamental tasks that whole body of the robot must be cooperatively controlled were conducted to test the usability in each CAA pattern. From the results of analysis, we found that pattern 1, which one operator controls front two arms, flippers, and crawlers, and another operator controls remaining back parts, had best scores for all tasks, as well as pattern 2 had the best distribution of workload between two operators.

Traffic complexity is one of the factors affecting driver workload. In order to study the relationship between traffic complexity levels and workload, a designed experiment is required, especially to vary traffic flow parameters systematically in a simulated environment. This paper describes the experimental design of a simulator study for developing a computational model to estimate the behavior of driver workload based on traffic complexity. Driving simulators allow creating and testing different traffic scenarios and manipulating independent variables to improve the quality of data, as compared to real world experiments. Physiological responses such as heart rate, skin conductance, and pupil size have been found to be related to workload. By adapting a data-driven method, we integrated electrocardiography sensors, electro-dermal activity sensors, and eye-tracker to acquire driver physiological signals and gaze information. Preliminary results show a positive correlation between traffic complexity levels and corresponding physiological responses, performance, and subjective measures.

Gait-training robots are used to improve gait performance by assisting motion. A robotic assistance method to encourage people to learn an induced gait is required, and robotic control methods of assistance as needed to enhance the active participation of patients have been studied. In this paper, we propose an intermittent force control method with a wire-driven gait-training robot to encourage people to learn the induced gait. We focused on a force-assisting knee flexion with increased toe clearance. We used an assistance timing detection method for the robot, conducting tensile force control based on information from the hip, knee, and ankle angles. The gait-training robot controlled the wire tensile force by controlling the motor rotation, and could switch between a mode in which force was applied and a mode in which force was not applied. We investigated the effect of the frequency of force application on the change in knee flexion angle after the gait-training robot stopped intervention. We confirmed that intermittent force application that did not assist every gait cycle was more beneficial in encouraging people to learn the induced gait than force application during every gait cycle.

Ascertaining a person's motion intentions through muscle activity is important for controlling various assistive devices for people with disabilities. Several techniques have been proposed for estimating the extent of intended joint angle motion using skin deformation information derived from muscle contractions. The objective of this study is to verify our signal processing procedure for estimating intended wrist joint angle with skin deformation information in able-bodied subjects and subjects with an upper-limb amputation. Skin deformation was measured with a tactile sensor consisting of 48 distance sensors over a large measurement area. The root-mean-square error (RMSE) of the measured and estimated angles are evaluated offline using multiple linear regression in one individual with an upper-limb amputation and five able-bodied participants. In all tests, subjects undertook a wrist flexion and extension task guided by visual feedback, measured in real time. Sensors are selected in descending order of the standard deviation of each sensor's value. Strong relationships occur between the position and displacement of the area of greatest skin deformation and the intended wrist joint angle in all subjects. The minimum RMSE was 8.19° for the individual with an upper-limb amputation using 48 sensors as input, and 2.24° for able-bodied individuals using 16 sensors. One-way repeated-measures analysis of variance showed that at least 16 sensors are needed to reliably record skin deformation. Skin deformation analyzed with multiple linear regression is a plausible means of estimating intended wrist joint angle in persons with an upperlimb amputation. Even when a limited number of sensors (≥16) are used, continuous joint angle can be estimated reliably. These findings will inform the design of assistive devices that must noninvasively determine muscle activity.

Force sensing is a crucial task for robots, especially when end effectors such as fingers and hands need to interact with unknown environments
to sense such forces, a force-torque (F/T) sensor is an essential component. In this paper, we propose a small-sized 6-axis F/T sensor with a novel arrangement of 12 transducers using the force transducer we have previously developed. The copper beryllium used in our sensor reduces hysteresis in each transducer. Additionally, the sensor provides digital output via I2C bus to reduce the susceptibility to noise, and reduce the number of required wires. Sensor characteristics such as its sensitivity, signal-to-noise ratio, linearity, and hysteresis are determined. More importantly, we showed that our sensor can detect and measure the 6-axis F/T.

Disaster response crawler robot OCTOPUS has four arms and four flippers for better adaptability to disaster environments. To further improve the robot mobility and terrain adaptability in unstructured terrain, we propose a new locomotion control method called compound motion pattern (CMP) for multi-limb robots like OCTOPUS. This hybrid locomotion by cooperating the arms and flippers would be effective to adapt to the unstructured terrain due to combining the advantages of crawling and walking. As a preliminary study on CMP, we proposed a fundamental and conceptual CMP while clarifying problems in constructing CMP, and developed a semi-autonomous control system for realizing the CMP. Electrically-driven OCTOPUS was used to verify the reliability and correctness of CMP. Results of experiments on climbing a step indicate that the proposed control system could obtain relatively accurate terrain information and the CMP enabled the robot to climb the step. We thus confirmed that the proposed CMP would be effective to increase terrain adaptability of robot in unstructured environment, and it would be a useful locomotion method for advanced disaster response robots.

In this study, we synthesized a self-healing electrically conductive gel based on Agar/hydrophobically associated polyacrylamide (HPAAm) by a restraint-assisted method. Recently, self-healing conductive materials based on such gels have been widely researched. However, as gels are generally weak, the gel-based materials are also often low in strength. Therefore, in this study, we applied the restraint method for adding pyrrole to Agar/HPAAm, which is a self-healing high-strength gel. The synthesized product was a high-strength conductive gel with self-healing abilities. Tensile tests confirmed that the swelling of the synthesized gel caused a low tensile breaking stress. Additionally, electrical conductivity measurements showed that the conductivity was increased by the addition of polypyrrole. These measurements were also carried out after the gel underwent self-healing. 30% of the strength and 54% of the conductivity of the undamaged gel were recovered, indicating the good self-healing performance of the gel proposed in this research.

This paper proposes a basic near-environmental recognition framework based on groping for risk-tolerance disaster response robot (DRR). In extreme disaster sites, including high radiation and heavy smog, external sensors such as cameras and laser range finders do not work properly, and such sensors may be broken in accidents in the tasks. It is hoped that DRRs can continue to perform tasks, even if the external sensors cannot work, and at least, they can safely evacuate from the site. In this preliminary study, for recognizing near environments without using external sensors, we proposed a groping method. In this method, a robot actively touches the environment using arms or other movable parts, records the contact information, and then reconstructs a three-dimensional local map around the robot by the detected information, e.g., robot arm's position and reactive force. The proposed groping system can recognize the existence of three situations, such as an object, step, and pit, and those geometry, by exploring the designated space using arms. The groping strategy was designed considering both robot specification, time limitation, and required resolution. Experiments were performed using four-arm and four-crawler robot OCTOPUS. The results indicate that the proposed framework could recognize step, pit, and object, and calculate the position and size of the object, and confirm that the robot successfully removed the object on the basis of groped data.

In teleoperation of heavy machines, work efficiency will be 50% lower than manned operation because operators cannot obtain effective information about work sites due to the limitation of current monitoring systems. Operators would have opportunities to obtain such information before work (about a week required to introduce teleoperation systems) and during work. As a fundamental study to support operator's spatial cognition, we developed views to provide spatial information of work sites before work. Humans have cognitive maps which are created based on knowledge acquired from survey and route perspectives. To make operators acquire the above two knowledge, we provide a bird's-eye view that can be changed by operators to acquire a knowledge from survey perspective, and a view from operator's viewpoint that can be changed by operator's intention to acquire a knowledge from route perspective. To evaluate two pre-offering views, we preformed experiments using a virtual reality simulator. The results indicated that a view to acquire a knowledge from survey perspective could help operators plan totally and one to acquire a knowledge from route perspective could help operators plan locally, and could increase work efficiency.

Torque limiters are a proven way to enhance the safety in robots. To further increase the safety, adjustable torque limits depending on the task and the joint configuration (joint angles, velocity, acceleration) would be preferable. Friction clutches can be used as adjustable torque limiters (ATL). In contact free motion the ATL can be set with torque limits higher than the required torque, thereby not influencing the position tracking performance. At an impact, the torque is intrinsically limited, enhancing the safety. Furthermore, depending on the implementation, friction clutches have another relevant property. They can have different torque limits for static and kinetic friction: When the static torque limit is exceeded (as it would be the case in an incidental contact situation), the clutch starts slipping, and the torque output automatically decreases, thereby reducing the forces in a quasi-static contact, as defined in ISO/TS 15066:2016. The current paper implements and profiles an ATL, which exhibits a kinetic torque limit of only 50.4% of the static torque limit at 10rpm. This ensures both an adjustable torque limit fitting to the task requirement and a lower but not zero torque after impact for enhanced safety. Impact experiments validate the safety benefits outlined above.

Static stretching is widely performed to decrease muscle tone as a part of rehabilitation protocols. Finding out the optimal duration of static stretching is important to minimize the time required for rehabilitation therapy and it would be helpful for maintaining the patient's motivation towards daily rehabilitation tasks. Several studies have been conducted for the evaluation of static stretching
however, the recommended duration of static stretching varies widely between 15-30 s in general, because the traditional methods for the assessment of muscle tone do not monitor the continuous change in the target muscle's state. We have developed a method to monitor the viscoelasticity of one muscle continuously during static stretching, using a wearable indentation tester. In this study, we investigated a suitable signal processing method to detect the time required to change the muscle tone, utilizing the data collected using a wearable indentation tester. By calculating a viscoelastic index with a certain time window, we confirmed that the stretching duration required to bring about a decrease in muscle tone could be obtained with an accuracy in the order of 1 s.

Gait training robots are useful for changing gait patterns and decreasing risk of trip. Previous research has reported that decreasing duration of the assistance or guidance of the robot is beneficial for efficient gait training. Although robotic intermittent control method for assisting joint motion has been established, the effect of the robot intervention timing on change of toe clearance is unclear. In this paper, we tested different timings of applying torque to the knee, employing the intermittent control of a gait training robot to increase toe clearance throughout the swing phase. We focused on knee flexion motion and designed a gait training robot that can apply flexion torque to the knee with a wire-driven system. We used a method of timing detecting for the robot conducting torque control based on information from the hip, knee, and ankle angles to establish a non-time dependent parameter that can be used to adapt to gait change, such as gait speed. We carried out an experiment in which the conditions were four time points: starting the swing phase, lifting the foot, maintaining knee flexion, and finishing knee flexion. The results show that applying flexion torque to the knee at the time point when people start lifting their toe is effective for increasing toe clearance in the whole swing phase.

In a previous study, we have developed a four-arm four-crawler disaster response robot called 'OCTOPUS'. Advanced disaster response robots are expected to be capable of both mobility, such as entering narrow spaces over unstructured ground, and workability, such as preforming complex debris-removal work. We have confirmed experimentally that the four arms could make the robot perform complex tasks while ensuring stabilization when climbing steps while the four flippers could make it traverse rough terrain while avoiding toppling over when conducting manipulation task. OCTOPUS, renamed as H-OCTOPUS, is oil-hydraulically driven to perform outdoor demolition of heavy debris, and is teleoperated by two operators. In this study, we develop a prototype electrically-driven OCTOPUS, called E-OCTOPUS, to manipulate various light-objects mainly indoors such as valve operations in nuclear power plants. For reducing the size and weight while maximizing task performance, we introduced a mutual complementary strategy between its arms and flippers. To validate the capability of E-OCTOPUS, we performed preliminary experiments involving climbing high steps and manipulating and cutting wires by cooperating the four arms and four flippers. The results indicated that E-OCTOPUS could complete the tasks by coordinating its four arms and four flippers.

Intelligent vehicles operating in different levels of automation require the driver to fully or partially conduct the dynamic driving task (DDT) and to conduct fallback performance of the DDT, during a trip. Such vehicles create the need for novel human-machine interfaces (HMIs) designed to conduct high-level vehicle control tasks. Multimodal interfaces (MMIs) have advantages such as improved recognition, faster interaction, and situation-Adaptability, over unimodal interfaces. In this study, we developed and evaluated a MMI system with three input modalities
touchscreen, hand-gesture, and haptic to input tactical-level control commands (e.g. lane-changing, overtaking, and parking). We conducted driving experiments in a driving simulator to evaluate the effectiveness of the MMI system. The results show that multimodal HMI significantly reduced the driver workload, improved the efficiency of interaction, and minimized input errors compared with unimodal interfaces. Moreover, we discovered relationships between input types and modalities: location-based inputs-Touchscreen interface, time-critical inputs-haptic interface. The results proved the functional advantages and effectiveness of multimodal interface system over its unimodal components for conducting tactical-level driving tasks.

This paper proposes a practical scheme for measuring the mass of an object grasped by the end-effector of a large-scale hydraulic manipulator. Such a measurement system requires high accuracy and robustness considering the nonlinearity and uncertainty in hydraulic pressure-based force measurement during rigorous outdoor work. It is thus difficult to precisely model system behaviors and completely remove error force components (white-box modeling) under such conditions, so our scheme adopts a less-error data selection approach to relatively improving the accuracy and reliability of the measurand (gray-box modeling). It first removes dominant error forces, i.e., gravity and dynamic friction forces, then defines the on-load state by evaluating measurement conditions to omit data in indeterminate conditions, then extracts data during the object-grasp state identified by a grasp motion model and removes high-frequency components by a simple low-pass filter, and finally integrates data from multiple sensors using the posture-based priority and averages all selected data. Evaluation experiments were conducted using an instrumented hydraulic arm. Results indicate that our scheme can precisely measures the mass of the grasped object under various detection conditions with fewer errors.

We propose a tool-body assimilation model that considers grasping during motor babbling for using tools. A robot with tool-use skills can be useful in human robot symbiosis because this allows the robot to expand its task performing abilities. Past studies that included tool-body assimilation approaches were mainly focused on obtaining the functions of the tools, and demonstrated the robot starting its motions with a tool pre-attached to the robot. This implies that the robot would not be able to decide whether and where to grasp the tool. In real life environments, robots would need to consider the possibilities of tool grasping positions, and then grasp the tool. To address these issues, the robot performs motor babbling by grasping and nongrasping the tools to learn the robot's body model and tool functions. In addition, the robot grasps various parts of the tools to learn different tool functions from different grasping positions. The motion experiences are learned using deep learning. In model evaluation, the robot manipulates an object task without tools, and with several tools of different shapes. The robot generates motions after being shown the initial state and a target image, by deciding whether and where to grasp the tool. Therefore, the robot is capable of generating the correct motion and grasping decision when the initial state and a target image are provided to the robot. (C) 2017 The Authors. Published by Elsevier B.V.

A new magnetorheological piston head design inspired by toroidal electromagnets was proposed in previouswork as an alternative to conventional annular dampers. A prototype with a circular valve array integrated inside the piston head was built and tested to measure its passive performance. The mechanical, electromagnetic, and hydraulic models used in the new design were explained, and the relevant parameters of the actuator were analyzed to construct a mathematical model to estimate its performance. These works showed the feasibility of the concept and its potential as an alternative to current damper technology, but lacked a baseline for comparison. This paper reviews and widens this groundwork. It adds a magnetic finite element method study to address the previously found leakage, and a new set of experiments, including a force controller, to compare its performance against a conventional annular piston head. The new findings show how the current force limitations can be overcome by striking a balance between the coil space and the size of electromagnet cores to achieve the performance of current dampers. They also highlight its potential in force control applications to provide a wider range of customization options, such as number and size of holes, electromagnets, and coils, and a better use of the active area of the gap.

We suggest that different behavior generation schemes, such as sensory reflex behavior and intentional proactive behavior, can be developed by a newly proposed dynamic neural network model, named stochastic multiple timescale recurrent neural network (S-MTRNN). The model learns to predict subsequent sensory inputs, generating both their means and their uncertainty levels in terms of variance (or inverse precision) by utilizing its multiple timescale property. This model was employed in robotics learning experiments in which one robot controlled by the S-MTRNN was required to interact with another robot under the condition of uncertainty about the other's behavior. The experimental results show that self-organized and sensory reflex behavior-based on probabilistic prediction-emerges when learning proceeds without a precise specification of initial conditions. In contrast, intentional proactive behavior with deterministic predictions emerges when precise initial conditions are available. The results also showed that, in situations where unanticipated behavior of the other robot was perceived, the behavioral context was revised adequately by adaptation of the internal neural dynamics to respond to sensory inputs during sensory reflex behavior generation. On the other hand, during intentional proactive behavior generation, an error regression scheme by which the internal neural activity was modified in the direction of minimizing prediction errors was needed for adequately revising the behavioral context. These results indicate that two different ways of treating uncertainty about perceptual events in learning, namely, probabilistic modeling and deterministic modeling, contribute to the development of different dynamic neuronal structures governing the two types of behavior generation schemes.

© 2017 IEEE. Effective design and fabrication of 3-D electronic circuits are among the most pressing issues for future engineering. Although a variety of flexible devices have been developed, most of them are still designed two-dimensionally. In this letter, we introduce a novel idea to fabricate a 3-D wiring board. We produced the 3-D wiring board from one desktop inkjet printer by printing conductive pattern and a 2-D pattern to induce self-folding. We printed silver ink onto a paper to realize the conductive trace. Meanwhile, a 3-D structure was constructed with self-folding induced by water-based ink printed from the same printer. The paper with the silver ink self-folds along the printed line. The printed silver ink is sufficiently thin to be flexible. Even if the silver ink is already printed, the paper can self-fold or self-bend to consist the 3-D wiring board. A paper scratch driven robot was developed using this method. The robot traveled 56 mm in 15 s according to the vibration induced by the electrostatic force of the printed electrode. The size of the robot is 30 × 15 × 10 mm. This work proposes a new method to design 3-D wiring board, and shows extended possibilities for printed paper mechatronics.

This paper proposes a way to protect endangered wayang puppet theater, an intangible cultural heritage from Indonesia, by turning a robot into a puppeteer successor. We developed a seven degrees-offreedom (DOF) manipulator to actuate the sticks attached to the wayang puppet body and hands. The robot can imitate 8 distinct human puppeteer’smanipulations. Furthermore, we developed a gamelan music pattern recognition, towards a robot that can perform based on the gamelan music. In the offline experiment, we extracted energy (time domain), spectral rolloff, 13 Mel-frequency cepstral coefficients (MFCCs), and the harmonic ratio from 5 s long clips, every 0.025 s, with a window length of 1 s, for a total of 2576 features. Two classifiers (3 layers feed-forward neural network (FNN) and multi-class Support Vector Machine (SVM)) were compared. The SVMclassifier outperformed the FNN classifier with a recognition rate of 96.4%for identifying the three different gamelan music patterns.

<p>Robots working in the real environment need to respond to necessary sensory inputs. However, if the sensory inputs are not necessary for action generation, robots need to stably generate action without being affected by unnecessary sensory inputs. To realize such adaptive action generation against situational changes, robots should automatically decide how much sensory inputs are necessary for action generation. In this research, we propose a method which automatically decides the ratio of actual and predicted sensory inputs based on predicted sensory uncertainty. As a result of robot experiments, the robot with proposed mechanism could conduct adaptive action generation against situational changes.</p>

The gas pipelines need to be inspected in the case of existence of pipeline's inner problems. Usually, inspection for leakage of gas pipeline has been carried out by wired probes or wired robots equipped with a CCD camera. However, the above inspection approaches are restricted by the cable in the pipe. This study thus focuses on an inspection approach based on microwave communication. However, obtaining the transmission properties (i.e., transmission loss, RSSI (received signal strength indication), maximum transmission distance, etc) in gas pipeline is crucial to determine which frequency is feasible to the further development. In this paper, a communication system for microwave communication in 2.4 and 5.8 GHz band is developed. Then, the transmission loss and RSSI of these microwaves in 2.5 x 100 cm (straight), 5 x 100cm (straight) and 5 x 200cm M-type curved steel gas pipe (with L-junctions) are then measured. The experimental results revealed that the microwave in 2.4 GHz band owns much stronger communication capability in complicated gas pipeline in consideration of few transmission loss, and three approximate linear functions could be modeled on the basis of the measured data.

In this paper, we propose a method to dynamically modulate the input state of recurrent neural networks (RNNs) so as to realize flexible and robust robot behavior. We employ the so-called stochastic continuous-time RNN (S-CTRNN), which can learn to predict the mean and variance (or uncertainty) of subsequent sensorimotor information. Our proposed method uses this estimated uncertainty to determine a mixture ratio for combining actual and predicted sensory states of network input. The method is evaluated by conducting a robot learning experiment in which a robot is required to perform a sensory-dependent task and a sensory-independent task. The sensory-dependent task requires the robot to incorporate meaningful sensory information, and the sensory-independent task requires the robot to ignore irrelevant sensory information. Experimental results demonstrate that a robot controlled by our proposed method exhibits flexible and robust behavior, which results from dynamic modulation of the network input on the basis of the estimated uncertainty of actual sensory states.

<p>In this study, we propose a novel endoscopic manipulation system that is controlled by a surgeon's eye movements. The optical axis direction of the endoscopic manipulator is altered intuitively based on the surgeon's pupil movements. A graphical user interface was developed by dividing the monitor screen into several areas with shape boundaries, so that the movement direction of the endoscope can be identified by the area gazed at by the operator. We used a probabilistic neural network (PNN) to decide the regional distribution proportion to recognize the direction in which the operator would want the endoscopic manipulator to move. The PNN model was trained by individual calibration data. We hypothesized that PNN model training could be completed immediately after calibration, which also determines the boundary of the regional distribution portion (RDP). We designed an experiment and recorded the path of direction change to verify the PNN's effectiveness in our proposed system. All participants, including four who wore glasses, completed the requested task. Moreover, wearing glasses had no significant effect on the performance of the proposed system. Furthermore, the PNN training duration only took 2% of the entire time of the procedure to handle individual differences. We conclude that our method can handle individual differences in operators' eyes through machine learning of personal calibration data over a short time frame, which will not take significant extra preoperative setup time.</p>

This paper proposes a learning strategy for robots with flexible joints having multi-degrees of freedom in order to achieve dynamic motion tasks. In spite of there being several potential benefits of flexible-joint robots such as exploitation of intrinsic dynamics and passive adaptation to environmental changes with mechanical compliance, controlling such robots is challenging because of increased complexity of their dynamics. To achieve dynamic movements, we introduce a two-phase learning framework of the body dynamics of the robot using a recurrent neural network motivated by a deep learning strategy. The proposed methodology comprises a pre-training phase with motor babbling and a fine-tuning phase with additional learning of the target tasks. In the pre-training phase, we consider active and passive exploratory motions for efficient acquisition of body dynamics. In the fine-tuning phase, the learned body dynamics are adjusted for specific tasks. We demonstrate the effectiveness of the proposed methodology in achieving dynamic tasks involving constrained movement requiring interactions with the environment on a simulated robot model and an actual PR2 robot both of which have a compliantly actuated seven degree-of-freedom arm. The results illustrate a reduction in the required number of training iterations for task learning and generalization capabilities for untrained situations.

This paper addresses the general methods for creating the approximately optimal closed loop stabilization controllers that depend on given rigid body systems. The optimal stabilization controllers calculate the optimal force (torque) inputs that depend on the current states of the systems. In this paper, a creation method for approximately optimal controllers named closed loop optimizer based on reverse time tree (CLO-RTT) is proposed. In the open loop optimization phase, this method creates approximately optimal open loop solutions using rapid semi-optimal motion planning (RASMO). In the closed loop optimization phase, this method selects a solution from the RTT according to the measured current state of a system. The proposed method was validated in the time optimal stabilization problem of a double inverted pendulum model. The proposed method successfully stabilized the model quickly. When the resolution of RASMO was higher, the motion time was shorter.

© 2016, Cao et al. Increased attention has been focused on laparoscopic surgery because of its minimal invasiveness and improved cosmetic properties. However, the procedure of laparoscopic surgery is considerably difficult for surgeons, thus paving the way for the introduction of robotic technology to reduce the surgeon’s burden. Thus, we have developed a single-port surgery assistive robot with a master–slave structure that has two surgical manipulators and a sheath manipulator for the alteration of endoscope direction. During the development of the surgical robotic system, achieving intuitive operation is very important. In this paper, we propose a new laparoscope manipulator control system based on the movement of the pupils to enhance intuitive operability. We achieve this using a webcam and an image processing method. After the pupil movement data are obtained, the master computer transforms these data into an output signal, and then the slave computer receives and uses that signal to drive the robot. The details of the system and the pupil detection procedure are explained. The aim of the present experiment is to verify the effectiveness of the image processing method applied to the alteration of endoscope direction control system. For this purpose, we need to determine an appropriate pupil motion activation threshold to begin the sheath manipulator’s movement. We used four kinds of activation threshold, measuring the time cost of a particular operation: to move the image of the endoscope to a specific target position. Moreover, we identified an appropriate activation threshold that can be used to determine whether the endoscope is moving.

This technical note addresses a tradeoff in the tree based trajectory optimization algorithms for open-loop optimal control problem of rigid body system. In this technical note, linear prediction based uniform state sampling method (LPUSS), which relaxes the tradeoff between solutions' quality and computational efficiency, is proposed on the basis of the local linearity of motion equations. LPUSS has a lower calculation order than randomized kinodynamic planning (RKP) and rapid semi-optimal motion planning (RASMO). In the validation using multi degree-of-freedom (DOF) under actuated manipulator models, the solutions' quality and the computational efficiency of LPUSS were better than those of RKP and RASMO. LPUSS finished the optimization for the 6 DOF model within 40 minutes. This was the world's first success of the optimization of the swing up motion of a 6 DOF under actuated manipulator model.

This paper presents an easy means to produce a 3-axis Hall effect-based skin sensor for robotic applications. It uses an off-the-shelf chip and is physically small and provides digital output. Furthermore, the sensor has a soft exterior for safe interactions with the environment; in particular it uses soft silicone with about an 8 mm thickness. Tests were performed to evaluate the drift due to temperature changes, and a compensation using the integral temperature sensor was implemented. Furthermore, the hysteresis and the crosstalk between the 3-axis measurements were evaluated. The sensor is able to detect minimal forces of about 1 gf. The sensor was calibrated and results with total forces up to 1450 gf in the normal and tangential directions of the sensor are presented. The test revealed that the sensor is able to measure the different components of the force vector.

Indoor positioning remains an open problem, because it is difficult to achieve satisfactory accuracy within an indoor environment using current radio-based localization technology. In this study, we investigate the use of Indoor Messaging System (IMES) radio for high-accuracy indoor positioning. A hybrid positioning method combining IMES radio strength information and pedestrian dead reckoning information is proposed in order to improve IMES localization accuracy. For understanding the carrier noise ratio versus distance relation for IMES radio, the signal propagation of IMES radio is modeled and identified. Then, trilateration and extended Kalman filtering methods using the radio propagation model are developed for position estimation. These methods are evaluated through robot localization and pedestrian localization experiments. The experimental results show that the proposed hybrid positioning method achieved average estimation errors of 217 and 1846 mm in robot localization and pedestrian localization, respectively. In addition, in order to examine the reason for the positioning accuracy of pedestrian localization being much lower than that of robot localization, the influence of the human body on the radio propagation is experimentally evaluated. The result suggests that the influence of the human body can be modeled.

We propose an object grasping method with a real robot using a pre-training of grasping vector to predict grasping vectors of objects from two dimensions image by the Convolutional NeuralNetwork (CNN). Three dimensions image including RGB and depth data are obtained by a special camera and takes a long time to train the CNN. With the proposal method, CNN can learn grasping vectors efficiently against its time took and real robot generate grasping operation. To evaluate the method, we compared prediction accuracy of RGB image and gray-scale image using a real robot. The results show it is possible to perform a sufficient object grasping operation by the pre-training using RGB image.

We address the throwing motion optimization for robot. In order to pursue the best throwing motion, we may need heuristics/intuition free methods. We propose a throwing method that is composed of rapid semi-optimal motion-planning and output zeroing method. So as to execute the optimized trajectories in real rigid body systems, we need some compensations for the noises around the optimized trajectories. We introduce a compensation method for the optimized throwing motions of a robot arm with a free joint. To validate the effectiveness of the proposed method, we conducted a throwing experiment using a two-link arm. As a result of the experiment, the robot arm threw a ball with 63.7 km/h, which was the best record through the past experiments of this arm.

Disaster response robot with four arms and flippers OCTOPUS has high mobility and task-execution capabilities. Owing to the higher number of degrees of freedom, OCTOPUS is controlled by two operators, however this kind of robots is inherently difficult to be operated. To design easy-to-use human machine interfaces and intelligent control systems, we need to analyze and quantify a reasonable operation strategy in multi-operator control systems. Thus, three different types of essential disaster response tasks were conducted by using OCTOPUS and we analyzed results of operations and work performance, by focusing on each operator and each pair. As the results, we derived basic operation strategies as follow; operators with higher number of simultaneously-operated joints (Ns) can control OCTOPUS more smoothly, and pairs with higher rate of cooperated operations (Rc) can finish tasks more efficiently. We also found that Ns and Rc can be used to quantify operational skills. Revealed strategies and parameters could be useful to design new human-machine interface and intelligent control system.

A combined method of Doppler positioning and carrier-based hyperbolic positioning with a multi-channel GPS-pseudolite is proposed for indoor localization. This method uses carrier-phase output from a GPS/pseudolite receiver. The carrier-phase observable is precise but does not provide range information between the pseudolite and receiver antennas necessary for position calculation. This is because of the existence of carrier ambiguity. This problem can be solved by using the proposed combined method. In the present work, the positioning theory is established and experimentally evaluated with actual devices including a robot. The experimental result shows that a positioning accuracy of more than 10 cm is achievable.

The objective of this paper is to develop a method to estimate the wrist joint angle based on the deformation of the forearm skin surface during muscle contraction. We have focused on the longitudinal movement of the muscle bulge along the forearm. We previously confirmed a one-to-one relationship between the movement of the muscle bulge and the wrist joint angle, and validated the feasibility of the estimation of the joint angle using this relationship. However, the relationship between the movement of the muscle bulge and the wrist joint was previously difficult to perform because of the misalignment of the sensor on the muscle. Here we use a tactile sensor that can measure three-dimensional data on the forearm skin surface to map the muscle bulge location. We measured a large 32 x 96 mm area on the forearm skin with 48 skin distance sensors. We calculated x and y components of the barycentric coordinates from the measured data. We observed a one-to-one relationship between the y-component of the barycentric coordinate from the distribution of the movement of the muscle bulge on the forearm skin surface. We then calculated the RMSE between the measured and estimated wrist joint angle using our joint angle estimation algorithm. We found that the RMSE from our technique was greater than from the conventional method. While we validated the feasibility of our estimation method further research is required to reduce our estimation error by improving the extraction and interpretation of our sensor data.

This paper presents a virtual reality (VR) simulator for four-arm disaster response robot OCTOPUS, which has high capable of both mobility and workability. OCTOPUS has 26 degrees of freedom (DOF) and is currently teleoperated by two operators, so it is quite difficult to operate OCTOPUS. Thus, we developed a VR simulator for training operation, developing operator support system and control strategy. Compared with actual robot and environment, VR simulator can reproduce them at low cost and high efficiency. The VR simulator consists of VR environment and human-machine interface such as operation-input and video-and sound-output, based on robot operation system (ROS) and Gazebo. To enhance work performance, we implement indicators and data collection functions. Four tasks such as rough terrain passing, high-step climbing, obstacle stepping over, and object transport were conducted to evaluate OCTOPUS itself and our VR simulator. The results indicate that operators could complete all the tasks but the success rate differed in tasks. Smooth and stable operations increased the work performance, but sudden change and oscillation of operation degraded it. Cooperating multi-joint adequately is quite important to execute task more efficiently.

Elderly people are at risk of falling because of their low toe clearance. Gait training to improve toe clearance could be instrumental in avoiding tripping. We propose using a gait-training robot that applies torque during the pre-swing phase to achieve this goal. It is still possible to revert to their original trajectory after the training, however, depending on the magnitude of the applied torque. We investigated the relation between the magnitude of the applied torque and the change in toe clearance before and after application of torque. We developed a robot and carried out an experiment in which a motor pulls a string embedded on the robotic frame worn by the participants, thereby applying torque during the pre-swing phase. The experimental task included walking on a treadmill for 50 s. We applied torque to the knee during the pre-swing phase for 20 s. The phases before and after applying torque were 15-s normal walking phases with no interference from the robot. We compared toe clearance during the phases before and after applying torque. We found that the toe clearance increased after applying a torque of 8 Nm. We were thus able to verify the influence of torque on toe clearance.

A novel joint angle estimation method is proposed using a new bio-signal for an amputee subject. We used the muscle bulge movement on the forearm skin surface as a new bio-signal for estimating the extent of motion in a previous study. We found that it is feasible to estimate the intended wrist joint angle using the distribution of the muscle bulge for intact subjects. Thus, in the present paper, we validate the feasibility of our method for an amputee. In applying our method to an amputee subject, we improved our distance sensor device so that it can accommodate the position of the muscle, which is variable for an amputee subject. In addition, we improved the algorithm that estimates the wrist joint angle using linear multiple regression for calculating the relationship between the intended wrist joint angle and the distribution of the muscle bulge. As a result, we found that the distribution of the muscle bulge changes for the amputee as for intact subjects. The movement of the position of the muscle bulge on the forearm skin corresponded to the extent of the intended wrist joint angle. According to the result of the estimation of the wrist joint angle, the root-mean-square error of the estimated angle with respect to the measured angle for the amputee was slightly larger than the error for intact subjects. Nevertheless, the root-mean-square error for the amputee was smaller than that when employing the previous method for intact subjects. Finally, it is feasible to use the muscle bulge movement on the forearm skin to estimate the intended wrist joint angle for the upper limb amputee.

We developed a four-arm four-crawler advanced disaster response robot called OCTOPUS. Disaster response robots are expected to be capable of both mobility, e.g., entering narrow spaces over very rough unstable ground, and workability, e.g., conducting complex debris-demolition work. However, conventional disaster response robots are specialized in either mobility or workability. Moreover, strategies to independently enhance the capability of crawlers for mobility and arms for workability will increase the robot size and weight. To balance environmental applicability with the mobility and workability, OCTOPUS is equipped with a mutual complementary strategy between its arms and crawlers. The four arms conduct complex tasks while ensuring stabilization when climbing steps. The four crawlers translate rough terrain while avoiding toppling over when conducting demolition work. OCTOPUS is hydraulic driven and teleoperated by two operators. To evaluate the performance of OCTOPUS, we conducted preliminary experiments involving climbing high steps and removing attached objects by using the four arms. The results showed that OCTOPUS completed the two tasks by adequately coordinating its four arms and four crawlers and improvement in operability needs.

Elderly people are at risk of tripping because of their narrow range of articular motion. To avoid tripping, gait training that improves their range of articular motion would be beneficial. In this study we propose a gait-training robot that applies a torque during the pre-swing phase to achieve this goal. We investigated the relationship between magnitude of applied torque and change in the range of knee-articular motion while walking before and after the application of this torque. We developed a wearable robot and carried out an experiment on human participants in which a motor pulls a string embedded on the robotic frame, applying torque in the pre-swing phase for a period of 20 [s]. Before and after applying torque the participant walked normally for 15 [s] without interference from the robot. We found that knee flexion angle increased after applying the torque if the torque was within the range of approximately 6-8 [Nm]. Therefore, we were able to verify that a new range of knee articular motion can be learned through application of torque.

We are developing an endoscopic manipulator control system based on eye tracking, which uses pupil variation to improve control performance. In this paper, we propose a new approach for the control of camera zoom for an endoscopic manipulator using pupil variation, which has not been previously attempted. Before using pupil variation for the zoom function, we observed how pupil variation was related to controlling the timing for zooming in or out while a surgeon performed suturing tasks. After we confirmed the potential application for zoom control based on the variation of the size of pupil, we developed a zoom judge algorithm and integrated it into our endoscopic manipulator control system. We set up an experimental task to evaluate our proposal, and conclude that pupil variation has a significant effect on adjusting the zoom magnification for the endoscopic manipulator. Moreover, it shows better performance than zoom control by pedal input.

Human's ability to perceive intent plays a crucial role in achieving smooth and efficient navigation. At the present state, even with the state-of-the-art anthropomorphic robots, displaying human-like non-verbal communication (kinesics) is a challenging task. This poses a significant difficulty in performing legible navigation behavior for robots. In this paper, we look into light (turn indicator) and screen (arrow indicator) indicators as a means of overcoming the shortcomings of the robot's non-verbal communication abilities. Our results show a statistically significant improvement in perceived comfort, predictability, and performance with the use of light indicators.

This paper presents a low cost, easy to produce, small tactile sensor system, that can be embedded in a soft material and limited space. In the current implementation, we use a Hall-effect sensor and a magnet to measure the force. One sensor module can measure 3D force vector and temperature. This chip is planted inside a 55 x 55 x 8 mm of the silicon layer. The module has I2C digital output, requiring only four wires for each module. The experiment shows that the signal to noise ratio (SNR) for this module is relatively high, 21.4658 dB when 20g load is applied. The experiment also indicates that the sensor module measured loads differently depending on the type of material that is in contact.

This study aims to develop a fingertip in order to improve the manipulation performance of posture interpolation control for a multifingered robot hand.Increasing the contact area between the fingertips and object is one solution for improving grasping and manipulation stability.A reasonable fingertip shape design and material selection can increase the contact area.In this paper, we compare an anthropomorphic shape to a cylindrical body with a semispherical tip.In order to test the practical property such as contact area size of our proposed fingertip, a pressure experiment was conducted for comparing the two fingertip shapes under different workloads. Finally, we installed the fingertips on a commercial available robot hand Allegro Hand.Two tasks were conducted for evaluating our proposed fingertip with posture interpolation control. The results show that our proposed fingertip could improve manipulation performance by comparing the success rate in both the experiments.

In-hand manipulation is often needed to accomplish a practical task after grasping an object. In-hand manipulation of variously sized and shaped objects in multi-fingered hands without dropping the object is challenging. In this paper we suggest a combined strategy of force control and passive adaptation through soft fingertips with simple interpolation control to achieve in-hand manipulation between various postures and with various objects. While passive compliance can be achieved in numerous ways, this paper uses soft skin, as it does not require complex mechanisms and was easy to integrate in the robot hand (Allegro hand). Softness has proven to significantly ease object grasping, and the current paper shows the importance of softness also for in-hand manipulation. In particular, the simple interpolation strategy between various postures is successful when combined with soft fingertips, with or without force control, but fails with hard fingertips. Objects of varying size, shape and hardness were reliably manipulated. While the soft fingertips enabled good results in our experiments, a sufficiently precise definition of the postures and object size was required. When combining the interpolation control with a force control strategy, bigger errors in defining the posture and object size are possible, without deforming or dropping the object, and the resultant force is lower. As a result, we achieved robust in -hand manipulation between various postures and with objects of different size, shape and hardness.

This research aims to clarify and analyze potential factors in fingertip designs and conditions which may affect prehension performances in addition to the control strategy. In this paper, four factors of fingertip designs and conditions are considered: hardness, thickness of the skin, shape and the surface friction condition of the fingertip. Six fingertips were made in order to compare these factors and their influences on grasp stability under different conditions (high and low workload). An evaluation experiment was conducted in order to investigate how the proposed properties affect the grasping performance under different workloads. Results show an unexpected effectiveness of the soft skin grasping performance compared to less deformable materials, even with reduced friction; also the orientation of the fingertips significantly affects the grasping performance in the case of anthropomorphic fingertips. These results can be useful for designing a new robot hand and a control strategy, which can potentially lead to a better stability and efficient performance.

Integrating distributed sensors in the skin of robot hands is challenging, as the space is limited. This paper presents a dense and small tactile sensor system that can be installed on robotic hands. In the current implementation, the system is constituted by modules that are 26mm long and 27mm wide and they have been successfully integrated on the internal side of each finger phalange of the commercially available Allegro Hand (except the fingertips). Each sensor module contains 16 tri-axial taxels; each taxel is able to measure the applied 3D force vector using a Hall effect sensor and a magnet. The sensor modules are 4mm high, including the printed circuit board (PCB) with the sensors and the soft silicone with the magnets. The back of the PCB is flat without any components mounted, which eases the integration. Each sensor has I2C digital output, and each sensor module is connected to four I2C buses, requiring only seven wires for each module. The tri-axial taxels are close to each other (4.7 mm from the center of one taxel to the next), but experiments proved that independent force vectors can be measured and that the crosstalk is limited.

In this study, the concept of a self-repairing wire for tendon-driven robots was proposed. A wire in a tendon-driven robot is worn by friction between the tendon and pulley and also between the tendon and guide rail. A worn tendon may lead to breakage of the tendon and makes it difficult to control the robot. In this study, a method to continuously protect the wire surface was proposed to inhibit tendon abrasion. A polymer layer was formed around the catalyst by coating the surface of the wire with a catalyst and supplying liquid resin to the surface. The layer protected the wire from abrasion and the wire re-formed itself when it was worn by abrasion. Specifically, when the wire was abraded, the catalysts reacted with the liquid resin to re-form the coating. In the study, three experiments were conducted and the results confirmed that the wire was protected from abrasion by re-coating itself.

We propose a method of tool use considering the transition process of a body model from not grasping to grasping a tool using a single model. In our previous research, we proposed a tool-body assimilation model in which a robot autonomously learns tool functions using a deep neural network (DNN) and recurrent neural network (RNN) through experiences of motor babbling. However, the robot started its motion already holding the tools. In real-life situations, the robot would make decisions regarding grasping (handling) or not grasping (manipulating) a tool. To achieve this, the robot performs motor babbling without the tool pre-attached to the hand with the same motion twice, in which the robot handles the tool or manipulates without graping it. To evaluate the model, we have the robot generate motions with showing the initial and target states. As a result, the robot could generate the correct motions with grasping decisions.

This study demonstrates that the prediction error minimization (PEM) mechanism can account for the emergence of reciprocal interaction between two cognitive agents. During interactive processes, alternation of forming and deforming interactions may be triggered by various internal and external causes. We focus in particular on external causes derived from a dynamic and uncertain environment. Two small humanoid robots controlled by an identical dynamic neural network model using the PEM mechanism were trained to achieve a set of coherent ball-playing interactions between them. The two robots predict each other in a top-down way while they try to minimize the prediction errors derived from the unstable ball dynamics or the external cause in a bottom-up way by using the PEM mechanism. The experimental results showed that switching among the set of trained interactive ball plays between the two robots appears spontaneously. The analysis clarified how each complementary behavior can be generated via mutual adaptation between the two robots by undertaking top-down and bottom-up interaction in each individual dynamic neural network model by using the PEM mechanism.

Human communicative behavior is both dynamic and bidirectional. This study aims to analyze such behavior by conducting imitative interactions between human subjects and a humanoid robot that has a neuro-dynamical system. For this purpose, we take a robot-centered approach in which the change in robot performance according to difference in human partner is analyzed, rather than adopting the typical human-centered approach. A small humanoid robot equipped with a neuro-dynamical system learns imitative arm movement patterns and interacts with humans after the learning process. We analyze the interactive phenomena by different methods, including principal component analysis and use of a recurrence plot. Through this analysis, we demonstrate that different classes of interactions can be observed in the contextual dynamics of the neuro-dynamical system.

Autonomous vehicles will significantly change the existing driver-vehicle relationship, since only a destination input from the human driver will suffice. However, reduced degree of human-control could result in lack of driving pleasure and excitement. Thus, we proposed a method to increase the flexibility in controlling of an autonomous vehicle by allowing the driver to control its lateral and longitudinal motions with a time lag. We first derived a set of vehicle movements to improve driver experience and related them to a set of control functions that the driver can input. We then created two types of driver-vehicle interfaces (DVIs) for vehicle control; a haptic interface with kinesthetic and tactile feedback, and a hand-gesture interface with augmented reality feedback. The joystick-type haptic interface provides feedback on driver input by dynamically varying its degrees of freedom through controlling the current supplied to axis motors, and through vibration motors. The gesture interface is based on Leap Motion controller and provides visual feedback to driver. We conducted driving experiments in a VR simulator using twenty drivers to evaluate the effectiveness of these DVIs. The results showed that haptic interface significantly reduced the average input time and input error, and drivers preferred the haptic interface due to its ability to provide immediate, active, and passive feedback.

Autonomous vehicles would make the future roads safer by keeping the human driver out of the loop. However, reduced degree of human-control could result in loss of the feeling of driving for some drivers. Therefore, in this study we proposed a method of interaction between the driver and autonomous vehicle by allowing the driver to control the vehicle's lateral and longitudinal motions. We adopted hand gestures as input modality because it can reduce driver's visual and cognitive demands. We first derived seven fundamental vehicle maneuvers to improve driver experience, and related them to seven independent hand gestures. We then created a hand gesture interface to control an autonomous vehicle, using Leap Motion as the gesture recognition platform. We conducted driving experiments involving twenty drivers in a virtual reality driving simulator to investigate the effectiveness of this interface for vehicle control. We evaluated the driving experience and drivers' opinions regarding the gestural interface. The results proved that semi-autonomous controlling using the hand gesture interface significantly reduced drivers' perceived work-load.

In unmanned construction, work efficiency is lower than that in manned construction due to lack of visual information. Thus, we previously developed an autonomous camera control system to provide various visual information suited to work states through multiple displays. However, that system increased the cognitive load on operators, and required them to have much experience to choose appropriate views for various situations. Next, we should investigate the degree of effectiveness for each view in a certain state. Thus, in this study, we analyzed gaze patterns to clarify which are the displays that operators often watch in work states, i.e., moving, grasping, transport, and releasing. We then derived which gaze patterns have higher work performance, including time efficiency and safeness. We clustered gaze patterns using Ward's method, which is a criterion applied in hierarchical clustering. To evaluate the objective of this study, we conducted experiments involving debris transport tasks, using a virtual reality simulator. The results indicated that gaze patterns differed in operators and we found that better time efficiency related to specific gaze patterns for each work state.

The advantages of mechanical compliance have driven the development of devices using new smart materials. A new kind of magnetorheological piston based on a toroidal array of magnetorheological valves, has been previously tested to prove its feasibility. However, being an initial prototype its potential was still limited by its complex design, and low output force. This study presents the revisions done to the design with several improvements targeting key performance parameters. An improved annular piston design is also introduced as comparison with conventional devices. The toroidal and annular piston head prototypes are built and tested, and their force performance compared with the previous iteration. The experimental results show an overall performance improvement of the toroidal assembly. However, the force model used in the study still fails to accurately predict the magnetic flux at the gaps of the piston head. This deviation is later verify and corrected using a FEM analysis. The force performance of the new toroidal assembly is on par with the commonplace annular design. It also displays a more linear behaviour, at the expense of lower energy efficiency. Finally, it also shows potential for a greater degree of customisation to meet different system requirements.

Compliant actuation is an indispensable element for safe physical human robot interaction. However, there is a lack of devices, which can integrate the high power density of hydraulic actuators with intrinsically safe mechanisms. This drove the development a new type of hydraulic magnetorheological piston. This device includes a novel toroidal array of magnetorheological valves in its head. In previous studies, the system performance was evaluated only as a traditional damping system. Now, the piston is connected to a pump to create a hydraulic compliant actuator. This novel compliant piston is capable to control the piston speed and force independently by using the pump and electromagnet voltages respectively. In this way, different combinations of these parameters can be used to achieve diverse system properties; e.g. low response time or energy efficiency. Several experiments are conducted to evaluate its performance, including force, friction, speed, and step response. The results display the potential of the devices to be used as an active system for compliant hydraulic robotic applications. They also hint to the possibilities to improve by using a more sophisticated control system for its speed and force.

This work focuses on the analysis of the performance parameters of a new magneto-rheological device. Previously, a mathematical model of the actuator head was introduced; however, the complex relations between its parameters made its optimisation challenging, causing the device not to reach its full potential. In this work, the previous model is updated to fit a revised version of the toroidal design, as well as an annular design for comparison. The relations between different parameters are studied to find their trade-offs, and understand how they affect the performance of the actuators. Finally, an optimisation based on an iterative search for valid permutations of parameters, is used to find optimal combinations. Two prototypes are built and tested to validate the results of the optimisation. The study revealed the critical parameters of each design, which mostly depend on the relations between the magnetic flux density, and the active area exposed to the MRF; and successfully optimised the performance of the devices. However, work needs to be done to create a more complete tool with concrete guidelines for specific applications.

Legible robot behavior is a key element for smooth and efficient navigation among humans. However, at present, even the state-of-the-art robots cannot communicate its internal state of directional intent by displaying human-like non-verbal cues. In this paper, we explore various modes for communicating directional intent of a robot across three different scenarios as a means of overcoming the shortcomings of the robot's non-verbal communication abilities. Specifically, we look into turn indicators, display indicators, and their combinations with sound and investigate their effectiveness across different passing scenarios. Our study shows us that using auxiliary communicating methods significantly improves the perceived feelings of our dependent measures. Further, communicating intention also helps in improving cooperation. However, the effectiveness greatly varies with the modes and the passing scenarios. In the case of 90 degrees crossing scenario, even though participants have positive perceived feelings, this does not necessarily translate into smooth and efficient navigation.

Moving objects within the hand is challenging, especially if the objects are of various shape and size. In this paper we use machine learning to learn in-hand manipulation of such various sized and shaped objects. The TWENDY-ONE hand is used, which has various properties that makes it well suited for in-hand manipulation: a high number of actuated joints, passive degrees of freedom and soft skin, six-axis force/torque (F/T) sensors in each fingertip, and distributed tactile sensors in the skin. A dataglove is used to gather training samples for teaching the required behavior. The object size information is extracted from the initial grasping posture. After training a neural network, the robot is able to manipulate objects of untrained sizes and shape. The results show the importance of size and tactile information. Compared to interpolation control, the adaptability for the initial posture gap could be greatly extended. Final results show that with deep learning the number of required training sets can be drastically reduced.

In unmanned construction, operators watch multi views to know situations at a disaster site. However, such a vision system requires much cognitive load of the operators to determine monitors and its parts which they should watch according to situation. In order to decrease cognitive load, we developed a system to provide appropriate monitors and its parts by inducing visual attention using augmented reality.

In this paper, we analyzed spatial perception ability based on human eyesight for teleoperation. Recognizing the space relationship between different objects by human eyesight from a two dimensional monitor quickly and correctly is a hard task. On the basis of the analysis about visual perception error, we make some assumptions about the alignment of multi monitors. The reasons which cause visual perception error were then investigated using our virtual reality simulator,and experiments were implemented to verify our assumptions. Results show when objects were fixed, continuously moving the viewpoint around the fixed objects, the position of viewpoint obviously affected spatial perception accuracy of human subjects. We also found that subjects tend to estimate object position deeper than its real position.

Intelligent passenger vehicles with autonomous capabilities will be commonplace on our roads in the near future. These vehicles will reshape the existing relationship between the driver and vehicle. Therefore, to create a new type of rewarding relationship, it is important to analyze when drivers prefer autonomous vehicles to manually-driven (conventional) vehicles. This paper documents a driving simulator-based study conducted to identify the preferences and individual driving experiences of novice and experienced drivers of autonomous and conventional vehicles under different traffic and road conditions. We first developed a simplified driving simulator that could connect to different driver-vehicle interfaces (DVI). We then created virtual environments consisting of scenarios and events that drivers encounter in real-world driving, and we implemented fully autonomous driving. We then conducted experiments to clarify how the autonomous driving experience differed for the two groups. The results showed that experienced drivers opt for conventional driving overall, mainly due to the flexibility and driving pleasure it offers, while novices tend to prefer autonomous driving due to its inherent ease and safety. A further analysis indicated that drivers preferred to use both autonomous and conventional driving methods interchangeably, depending on the road and traffic conditions.

In this study, we focused on creating different scenarios and traffic conditions in a driving simulator to evaluate the differences in driving experience in conventional and autonomous vehicles. We conducted experiments using two groups of drivers and evaluated their subjective workload and preference for each driving method for different traffic conditions and scenarios.

In recent years there has been interest in the development of magnetorheological actuators for robotic applications with adjustable compliance. In this paper we explore the conceptualization, design and test of a new kind magnetorheological piston head design. Inside the head of this new damper a circular assembly of magnetorheological valves is used to control the damping force of the piston. The results show the feasibility of the new design, and its potential for compliant robotic applications.

This paper introduces a novel tri-axial capacitive force sensor. The sensor can measure the force vector, is embedded in soft 7mm-thick silicone skin, enables temperature sensitivity compensation and has digital output. To measure the force vector, tilted capacitive sensor elements are used which are facing in different directions to differentiate the tangential forces. The sensor is intended for distributed contact sensing in a robotic skin, but could be also used for other applications such as novel haptic user interfaces in wearable devices. A series of experiments was performed and showed good sensor characteristics. The concept of the tilted force transducers has been proven to have the capability of detecting the force vector acting on the local sensor surface.

Static stretching is one of the most popular ways to decrease muscle tone. Muscle tone is evaluated by muscle hardness. To evaluate how muscle tone decreases during static stretching, we have proposed a method that involves using a wearable muscle hardness meter. In a previous study on the rectus femoris, we sometimes observed a muscle hardness increase during self-static stretching. We did not consider the degree of elongation of the muscle; however, this does actually affect muscle hardness. In this paper, we investigate the effect of joint angle on muscle hardness in self-static stretching of the gastrocnemius. We measured the muscle hardness of the medial head and the lateral head of the gastrocnemius and the dorsiflexion angle of the ankle joint during static stretching. In some cases, when the joint angle increased during static stretching, muscle hardness tended to increase. This result suggests that we should consider muscle elongation when evaluating muscle hardness during static stretching.

A hyperbolic positioning method with antenna arrays consisting of proximately-located antennas and a multi-channel pseudolite is proposed in order to overcome the problems of indoor positioning with conventional pseudolites (ground-based GPS transmitters). A two-dimensional positioning experiment using actual devices is conducted. The experimental result shows that the positioning accuracy varies centimeter- to meter-level according to the geometric relation between the pseudolite antennas and the receiver. It also shows that the bias error of the carrier-phase difference observables is more serious than their random error. Based on the size of the bias error of carrier-phase difference that is inverse-calculated from the experimental result, three-dimensional positioning performance is evaluated by computer simulation. In addition, in the three-dimensional positioning scenario, an initial value convergence analysis of the non-linear least squares is conducted. Its result shows that initial values that can converge to a right position exist at least under the proposed antenna setup. The simulated values and evaluation methods introduced in this work can be applied to various antenna setups; therefore, by using them, positioning performance can be predicted in advance of installing an actual system.

In this paper, we have looked into some two behaviors for 'efficient space utilization' by humans in a congested situation. From observations, we have noticed that 'last minute avoidance' and 'shoulder turning' are two crucial behaviors in achieving efficient navigation in crowded environments. The presented study verifies the shoulder turning behavior and investigates the typical values for these behaviors. These results will form an initial framework for local avoidance planner for future research.

The recovery of motor function after stroke is widely considered to result from brain plasticity. However, what kind of training exercise can better provoke brain plastic processes is still unclear. Studying regional brain activation during a specific training exercise may provide value information that can help design more effective therapeutic approaches. In this paper, we monitored brain activation when subjects performed four different finger training exercises: left middle finger passive movement (LMFP), left middle finger active movement (LMFA), middle finger self-motion control movement (MF), and both middle fingers active movement (BMFA). Eight healthy volunteers were involved in this study. The results indicated that the LFMA results in stronger brain activation than the LMFP in the left insular, left inferior frontal cortex, right middle cingulate cortex, left S1, bilateral cerebellum, SMA, and right inferior frontal areas. However, we did not find a significant difference when comparing the the MF and the BMFA conditions.

This paper presents the design, development and magnetic resonance imagining (MRI) compatibility evaluation of a small size, compact and adjustable different finger phalange lengths rehabilitation device. This device employs ultrasonic motor as its actuator and adopts a novel six-link mechanism to drive the finger. The final system enables to provide two joints (the MCP and the PIP) in each finger to do flexion and extension motion with one degree of freedom (DOF). The MRI compatibility of the robot was also evaluated. The results demonstrate that there is neither an effect from the MRI environment on the robot performance, nor significant degradation on MRI images by the introduction of the robot in the MRI scanner. Finally, an fMRI study with subject was carried out, the result shows a stable brain activation was observed when the middle finger of the subject was driven to implement passive rehabilitation motion inside the MRI scanner.

We propose the new method of tool use with a tool-body assimilation model based on body babbling and a neurodynamical system for robots to use tools. Almost all existing studies for robots to use tools require predetermined motions and tool features; the motion patterns are limited and the robots cannot use novel tools. Other studies fully search for all available parameters for novel tools, but this leads to massive amounts of calculations. To solve these problems, we took the following approach: we used a humanoid robot model to generate random motions based on human body babbling. These rich motion experiences were used to train recurrent and deep neural networks for modeling a body image. Tool features were self-organized in parametric bias, modulating the body image according to the tool in use. Finally, we designed a neural network for the robot to generate motion only from the target image. Experiments were conducted with multiple tools for manipulating a cylindrical target object. The results show that the tool-body assimilation model is capable of motion generation.

This paper proposes a sensor model in three dimensional (3D) space, where the height of sensor above ground is variable and the position of sensor on the ground is limited. Traditional sensor models treat the sensor placement area as a film and there is no placement limits as well. As a consequence, the number of sensors is very large and the performance is not such ideal as simulation. In this paper, we develop a novel model for sensor placement, which makes sensors possible to be set at different height above ground, so that the coverage rate can be analyzed in 3D space and the simulation result can be more dependable. We also propose a corresponding 3D coverage rate evaluation method to be used for our proposed 3D model. In order to test our proposed model and evaluation method, the system is designed and numerical and graphical simulators are developed. Experiments are conducted to quantify trade-off relationship between coverage rate and cost, i.e., the number of sensors, which can make sensor placement strategy more practical. From the experimental results, we confirmed that the proposed 3D model and evaluation method is effective in a 3D space.

Eye-tracking technology has been applied to surgical skill improvement and can observe differences in gaze data between the experienced and novice surgeon. Some eye trackers can even detect pupil diameter. We hypothesized that differences in technical proficiency among surgeons affect the extent of changes in pupil diameter, because the higher mental workload a laparoscopic task requires, the larger the person's pupil diameter dilates. In this study, we recorded the pupil diameters of pediatric surgeons while they performed a suturing task in a dry box environment. The more proficient surgeons exhibited a smaller distribution in pupil diameter than did the less proficient surgeons. Our data indicate that an individual's suturing proficiency is closely related to the degree of dispersion of pupil diameter.

Intelligent vehicles capable of autonomous driving will be commercially available in near future. To formulate a beneficial relationship between driver and vehicle, it is important to analyze how the drivers would react to autonomous vehicles compared to human-driven (conventional) vehicles. In this study, we focused on analyzing individual driving experience in several road conditions for autonomous and conventional vehicles among experienced and novice drivers. We first developed a simplified driving simulator that can connect arbitrary interfaces, create virtual environments consisting of scenarios and events that drivers encounter in real-world driving, and implement fully autonomous driving. We then conducted experiments to clarify differences of driving experiences for autonomous driving between the two groups. The experimental results showed that experienced drivers opt for conventional driving mainly due to the flexibility and driving fun it offers, while novices tend to prefer autonomous driving due to its inherent easiness and safety. An in-depth analysis indicated that drivers preferred to use both the driving methods interchangeably depending on the road and traffic conditions.

This paper describes a combined tactile sensor and haptic interface that can change its stiffness using magnetorheological fluids (MR fluid). The tactile sensor consists of 6 distributed capacitive sensors that can sense the location and the amount of applied force. Above the sensors is a chamber filled with MR fluid. By changing the magnetic field, the hardness of the MR fluid, and thereby of the haptic interface, can be changed. Fast changes of the magnetization direction lead to a sensation of vibration. The resulting device can be used for novel haptic input devices or for robotic grippers. A prototype device has been constructed, and the effects of the varying magnetic field and the resulting varying stiffness of the MR fluid on the distributed force sensing with the capacitive sensors has been evaluated. We discovered that the measured forces vary very little with changes in the strength of the magnetic field.

Passive compliance is useful for robotic arms to ensure their safety. Often springs are used, but they are problematic because they reduce the achievable accelerations and can lead to underdamped oscillations. Torque limiters enhance the safety, but usually the torque limit cannot be adjusted to a desired torque. Electronically adjustable torque limiters, also known as series clutch actuators, have several benefits, especially for robotic arms, but they also have severe limitations. This paper suggests incorporating series clutch actuators into a gravity compensated arm. Consequently, gravity should not limit the isotropically achievable force anymore and in the case of power outage the arm keeps its position. The benefits and limitations of a series clutch actuator in a gravity compensated arm are discussed, and a prototype of such an arm is presented. Commercially available magnetic friction clutches are used. Preliminary experiments demonstrate that the safety can be increased.

Indoor positioning still remains as an open problem, for the reason that it is difficult to achieve a satisfactory level of accuracy within indoor environment, using current radio based localization technology. Thus we try to investigate the utilization of IMES radio for high accuracy indoor positioning, because of its higher resolution radio map. Signal propagation model of IMES radio has been well investigated, based on which algorithms of trilateration and extended kalman filter are developed for both static and dynamic localization of a mobile robot using IMES radio. Experiments results show that a higher localization accuracy could be achieved.

Dynamic interactions with caregivers are essential for infants to develop cognitive abilities, including aspects of action, perception, and attention. We hypothesized that these abilities can be acquired through the predictive learning of sensory inputs including their uncertainty (inverse precision) in terms of variance. To examine our hypothesis from the perspective of cognitive developmental robotics, we conducted a neurorobotics experiment involving a ball-playing interaction task between a human experimenter representing a caregiver and a small humanoid robot representing an infant. The robot was equipped with a dynamic generative model called a stochastic continuous-time recurrent neural network (S-CTRNN). The S-CTRNN learned to generate predictions about both the visuo-proprioceptive states of the robot and the uncertainty of these states by minimizing a negative log-likelihood consisting of log-uncertainty and precision-weighted prediction error. The experimental results showed that predictive learning with uncertainty estimation enabled the robot to acquire infant-like cognitive abilities through dynamic interactions with the experimenter. We also discuss the effects of infant-directed modifications observed in caregiver-infant interactions on the development of these abilities.

Unmanned construction machines are used after disasters. Compared with manned construction, time efficiency is lower because of incomplete visual information, communication delay, and lack of tactile experience. We have developed an autonomous camera control system to supply appropriate visual information. In order to let operator easy to avoid unexpected collision, we first introduce the frame for each viewport according to its contents. And then, we exchange the danger view with an overlook view. The purpose of this study is to attract operators' attention to the danger area when there is a probable collision if operator won't stop or change their action. Besides, we also want to reveal the relationship between gaze habit and performance. The experimental results conducted using our virtual reality simulator confirms that operator tends to watch danger view when it appears. In addition, work strategy may influence the danger avoidance and work time efficiency in opposite way.

As robots progressively continue to enter human lives, it becomes important for robots to navigate safely and efficiently in crowded environments. In fact, efficient navigation in crowded areas is an important prerequisite for successful coexistence between humans and robots. In this paper, we explore an unconventional idea wherein a robot tries to achieve a more efficient navigation by influencing an obstructing human to move away by means of contact. First, preliminary human reaction experiments were conducted wherein we established that we can successfully induce a human to move in a desired direction. Following this result, we have proposed a novel motion planning approach which considers inducement by contact. The system is then verified through simulation and real experiments. The results show us that the proposed method can be utilized for safer and more efficient navigation in a crowded, but relatively static environment.

In this paper, we propose a filter system that can selectively obtain the required substances from an artificial circulatory system. The blood circulatory system of the human body keeps us healthy. We consider this system to be useful for a robot system to maintain its condition. To realize an artificial circulatory system like a human body, it is important to deliver various substances with only a single vessel and extract only the required substances from them. We propose a method to deliver encapsulated substances. By using this method, each substance can be delivered without mixing for easy separation. We developed a filter system that can trap only the required substances without clogging based on differences in the capsule size. We conducted experiments to evaluate the separation performance of the system. We were able to obtain only the targeted substances with this system. We developed an impurity filtration system as an application of our system.

We propose a method for realizing effective dynamic motion learning in a flexible-joint robot using motor babbling. Flexible-joint robots have recently attracted attention because of their adaptiveness, safety, and, in particular, dynamic motions. It is difficult to control robots that require dynamic motion. In past studies, attractors and oscillators were designed as motion primitives of an assumed task in advance. However, it is difficult to adapt to unintended environmental changes using such methods. To overcome this problem, we use a recurrent neural network (RNN) that does not require predetermined parameters. In this research, we propose a method for facilitating effective learning. First, a robot learns simple motions via motor babbling, acquiring body dynamics using a recurrent neural network (RNN). Motor babbling is the process of movement that infants use to acquire their own body dynamics during their early days. Next, the robot learns additional motions required for a target task using the acquired body dynamics. For acquiring these body dynamics, the robot uses motor babbling with its redundant flexible joints to learn motion primitives. This redundancy implies that there are numerous possible motion patterns. In comparison to a basic learning task, the motion primitives are simply modified to adjust to the task. Next, we focus on the types of motions used in motor babbling. We classify the motions into two motion types, passive motion and active motion. Passive motion involves inertia without any torque input, whereas active motion involves a torque input. The robot acquires body dynamics from the passive motion and a means of torque generation from the active motion. As a result, we demonstrate the importance of performing prior learning via motor babbling before learning a task. In addition, task learning is made more efficient by dividing the motion into two types of motor babbling patterns.

Unmanned construction machines are used after disasters. Compared with manned construction, time efficiency is lower because of incomplete visual information, communication delay, and lack of tactile experience. We have developed an autonomous camera control system to supply appropriate visual information. In order to inform operators the potential data in the views, we introduced several augmented reality (AR) elements to induce visual attention to suitable images in a scenario. The purpose of this study is to develop an attention inducement system using AR technique and evaluate it under different conditions. We first improve our autonomous camera control system and then implement several kinds of AR items suitable for each work situation. The experimental results conducted using our virtual reality simulator confirm that AR items can supply a better positional relationship between machine and objects in the environment which makes operator handle the conditions as experienced. The results from eye-tracker data also indicate that AR items can induce visual attention to images suitable for situations.

A new design of a magnetorheological piston prototype intended for passive or active force control in robotic applications for human robot interaction is introduced. It is based in a novel toroidal array of valves, contained within the piston head, which are used to control the output force of the actuator in order to achieve a high degree of reliability, size efficiency, and safety, by exploiting the material properties of magnetorheological fluids and permalloy metals. This paper describes the main points in the development of the magnetorheological piston prototype, the mathematical modelling of the magnetic circuit, and the results of the experiments conducted using a universal testing machine to evaluate the passive performance of the prototype. Results show the feasibility and performance of the new toroidal magnetic circuit of the magnetorheological hydraulic piston prototype. Improvements in order to be able to test the active performance of the design together with a pump setup are proposed.

In this paper we introduce a prototype of a novel hall-effect based skin sensor for robotic applications. It uses a small sized chip that provides 3-axis digital output in a compact package. Our purpose was to evaluate the feasibility of measuring 3-axis force while maintain a soft exterior for safe interactions. Silicone was used to produce the soft skin layer with about 8 mm thickness. An MLX90393 chip was installed at the bottom of layer, with a small magnet approximately 5mm above it to measure 3-axial magnetic field data. To evaluate the sensor's performance, an experiment was conducted by measuring normal and shear force when applying total forces of 0.7-14N in the normal and tangential directions of the sensor. The test revealed that the sensor prototype was able to differentiate the components of the force vector, with limited crosstalk. A calibration was performed to convert the measurements of the magnetic field to force values.

In densely populated scenarios with cramped spaces, it is very difficult to achieve safe and efficient navigation without cooperation from humans. One way in which we can seek cooperation from humans is by using contact to influence them to give way. However, such a method may incur certain psychological implications and therefore requires an acceptability check to ensure whether such action is acceptable or not. For this purpose, we investigate the participant subjective response towards robot-initiated touch during the course of navigation. We conducted a 2 (robotic experience vs. none) x 2 (warning vs. none) between-subject experiment with 44 people in which a mobile robotic platform exerted contact on an unaware and obstructing participant to make way towards its goal. Our results show that prior experience with robots produces slightly better response even though the results are not statistically significant. However, a verbal warning prior to contact yielded much more favorable response. In general, the participants did not find contact to be uncomfortable and were not opposed to robot-initiated contact if deemed necessary.

We propose an exploratory form of motor babbling that uses variance predictions from a recurrent neural network as a method to acquire the body dynamics of a robot with flexible joints. In conventional research methods, it is difficult to construct real robots because of the large number of motor babbling motions required. In motor babbling, different motions may be easy or difficult to predict. The variance is large in difficult-to-predict motions, whereas the variance is small in easy-to-predict motions. We use a Stochastic Continuous Timescale Recurrent Neural Network to predict the accuracy and variance of motions. Using the proposed method, a robot can explore motions based on variance. To evaluate the proposed method, experiments were conducted in which the robot learns crank turning and door opening/closing tasks after exploring its body dynamics. The results show that the proposed method is capable of efficient motion generation for any given motion tasks.

This paper proposes a new soft capacitive-type 3-axis force sensor. The prototype version which can detect a multi-axis force vector is embedded inside 7mm-thick silicone skin, and provides digital output via an I2C bus. Tilted capacitive force transducers were used to measure the force vector; the transducers faced different directions to differentiate the tangential forces. Preliminary experiments were performed and the concept of the tilted force transducers has been proven to have the capability of differentiating the force vector acting on the sensor surface. (c) 2015 The Authors. Published by Elsevier B. V.

Object handling and manipulation are important functions of dexterous anthropomorphic hands. As it is difficult to create a manipulation model analytically for performing complex tasks as humans do, a simple way to accomplish generic in-hand manipulation is desirable for robotic hands, especially for robot hands with few sensors. In this paper, we designed standard grasping postures based on the grasping behavior of humans. Interpolation control is used to control the robot hand. The combination of these postures results in versatile object manipulation. This control strategy is evaluated by testing it with the 16 degrees of freedom Allegro Hand grasping different sizes of spheres.

IMES is an indoor messaging and positioning system. In the present work, three methods for improving the IMES transmitter are proposed based on the result of a radio acquisition analysis: transmission diversity, a variable beamwidth antenna, and a leaky coaxial cable (LCX). The transmission diversity method increases the possibility of signal acquisition in areas with weak signal. A small-sized variable beamwidth antenna is developed to mitigate the multipath interference in narrow spaces. A field experiment with a LCX in an actual train setup is conducted to verify the effect of the LCX for the positioning of moving vehicles. The evaluation results of the proposed methods suggest their potential to be applied to practical applications.

Bio-signal processing is a major topic in the field of robotics. Especially, prostheses have been studied for a long time to detect amputee's intentions from bio-signals because they do not have their limbs. However, controlling prostheses using bio-signals such as from the brain and surface electromyograms (sEMG), is complex and nonlinear because these signals are noisy and varied. It is not easy to determine the extent of motion such as a joint angle, and previous research used complex models or machine learning based on bio-signals. To estimate a joint angle easily and accurately, we propose a new bio-signal derived from the muscle bulge movements measured on the skin. We hypothesized a simple relationship between the muscle bulge movement longitudinally along a muscle and the corresponding joint angle, because the muscle contraction causes the change in the joint angle. We estimated a wrist joint angle as a function of the muscle bulge movement longitudinally along the extensor carpi radialis longus that is the agonist muscle of wrist extension. From the experimental results, the following three achievements were obtained. First, we extracted a linear relationship from the raw data with a high determination coefficient. Second, we showed that the estimation error using the proposed method was not much different from that of using sEMG in related work. Third, we established that the proposed method could estimate the joint angle robustly for the external loads on a limb.

A practical operator support system for disaster response work using construction machinery is applied to object-break operations using a dual-arm machine. An object-break operation, which separates an object into two different parts by applying massive pulling force, requires skillful force and trajectory controls. Operators typically have trouble visually perceiving the movement of the manipulator because the target object is strongly restrained. As a result, after the object has been separated into two pieces, the operator may continue performing lever operations because of unavoidable perception reaction time and the manipulator may collide with the surrounding environment. A control input cancellation system based on object-break identification is proposed to reduce the time difference between the object break and operation stoppage. A support system for disaster response situations must be safe and efficient and accommodate operators with various skill levels; thus, common physical phenomena related to the object breakage should be exploited. The system cancels control inputs to stop the movement of the manipulator when an object-break flag is detected, which is defined as a situation where the force applied to each joint of a manipulator decreases rapidly when a lever operation and load on the inside of the grapple are detected. Demolition experiments were conducted using a hydraulic dual-arm system. The results indicate that the displacement of the end-point after the object breaks is greatly decreased and the completion time of the task is reduced as well.

In this paper, an object grasp motion, which is a requisite condition to make a demolition machine grasp an object, is pragmatically modeled, considering accurate and robust identification. Grasping an object is a highly difficult task that requires safe and precise operations, particularly in disaster response work. Identifying a grasp or non-grasp state is essential for providing operational support. These types of outdoor machines lack visual and tactile sensors, so pragmatically available lever operation and cylinder pressure sensors are adopted as parameters for modeling. The grasp motion is simply defined by using sequential transitions of the on-off state of the operation signal and cylinder pressure data for the grapple and the manipulator. The results of experiments conducted to transport objects using an instrumented hydraulic arm indicated that the modeled grasp motion model effectively identifies a grasp or non-grasp state with high accuracy, independently of operators and work environments.

This paper proposes a quantification method for a comprehensive work flow in construction work for describing work states in more detail on the basis of analyzing state transitions of primitive static states (PSS), which consist of 16 symbolic work states defined by using on-off state of the lever operations and joint loads for the manipulator and end-effector. On the basis of the state transition rules derived from a transition-condition analysis, practical state transitions (PST), which are common and frequent transitions in arbitrary construction work, are defined. PST can be classified into essential state transition (EST) or nonessential state transitions (NST). EST extracts common phases of work progress and estimates positional relations between a manipulator and an object. NST reveals wasted movements that degrade the efficiency and quality of work. To evaluate comprehensive work flows modeled by combining EST and NST, work-analysis experiments using our instrumented setup were conducted. Results indicate that all the PSS definitely changes on the basis of PST under various work conditions, and work analysis using EST and NST easily reveals work characteristics and untrained tasks related to wasted movements.

This paper addresses a phase space partitioning problem in motion planning systems. A class of kinematic and dynamic motion planning systems, including rapid semioptimal motion-planning (RASMO), uses partitions for phase spaces in cumulative optimization criteria. In these systems, a partition results in a uniquely planned motion with a quality that is determined by a selected optimization criterion. In this paper, state-dispersion-based phase space partitioning (SDPP) that generates adaptive partitions is proposed. These partitions allow the motion planning systems to plan better motions. Uniform partitions and adaptively fixed partitions of SDPP are compared under several conditions using RASMO and a double inverted pendulum model while setting the optimality criterion of RASMO to time. The results reveal that RASMO with SDPP plans smaller time motions than those obtained with RASMO using uniform partitions.

This force masking humanoid robot system had been developed for a psychological experiment that confirms the rhythmic modalities required by humans when they play rope turning with a robot. Although a person feels many types of rhythms in physical rhythmic cooperation with a robot, what rhythms are required for the cooperation had not been investigated yet. This system is an effective tool to investigate the effectiveness of the force rhythm in physical cooperations.

We propose a Doppler pose estimation method for an indoor messaging system (IMES) ('pose' refers to both position and orientation). With this method, both the position and orientation of a receiver are estimated simultaneously by using Doppler shifts produced by moving a receiver antenna with two or more IMES transmitters. The proposed method is evaluated through the identical experiments conducted in two different locations. In these experiments, the position and orientation of the receiver is estimated using two transmitters, and the achievable accuracy is evaluated by changing the separation distance between the transmitter antennas. The experimental results demonstrate that a positioning accuracy higher than a few decimetres and orientation estimation accuracy of higher than a few degrees are achievable when the measurement condition is relatively good (i.e. when the proper separation distance is set between two transmitter antennas and cycle slips do not occur). We also conducted an analysis for the convergence of initial values (which are used for the iterative position and orientation calculation in the nonlinear least-squares method). The results show that the initial values basically converge to appropriate position and orientation values as long as an inverse matrix in the position and orientation estimation process can be calculated. Moreover, we analysed the effect of the number of transmitters on position and orientation estimation precision. The results show that, as the number of transmitters increases, the precision of the position and orientation estimation also increases, and the precision is particularly high in the area surrounded by the transmitters. © 2014 © 2014 Taylor &amp
Francis.

This paper describes a humanoid robot system that simultaneously controls the three factors, energy transmission, rotational axis and centrifugal force in rope turning tasks. As a result, this system realized both rope turning with one fixed end and with a human rope turner.

We have developed a simply designed robot for the purpose of the psychological research confirming human recognition of the boundaries of robot agents. The physical boundary and the agent's boundary of a robot are considered to have different shapes in human recognition. Results of the experiment indicated that we controlled the agent's boundary successfully.

Series elastic actuation (SEA) has been widely used for output force/torque regulation, however, the non-linearity introduced by its torsion spring hysteresis will greatly affect the quality of feedback control. Thus an experimental procedure is conducted for the modeling and identification of spring hysteresis effect, based on which a compensated control scheme is given. Experiment results show that the compensated control can improve the control performance compared with ordinary linear controller.

A method to autonomously control multiple environmental cameras, which are currently fixed, for providing more adaptive visual information suited to the work situation for advanced unmanned construction is proposed. Situations in which the yaw, pitch, and zoom of cameras should be controlled were analyzed and imaging objects including the machine, manipulator, and end-point and imaging modes including tracking, zoom, posture, and trajectory modes were defined. To control each camera simply and effectively, four practical camera roles combined with the imaging objects and modes were defined as the overview-machine, enlarge-end-point, posture-manipulator, and trajectory-manipulator. A role assignment system was then developed to assign the four camera roles to four out of six cameras suitable for the work situation, e.g., reaching, grasping, transport, and releasing, on the basis of the assignment priority rules, in the real time. Debris removal tasks were performed by using a VR simulator to compare fixed camera, manual control, and autonomous systems. Results showed that the autonomous system was the best of the three at decreasing the number of grasping misses and error contacts and increasing the subjective usability while improving the time efficiency.

In this paper, we propose a tool-body assimilation model that implements a multiple time-scales recurrent neural network (MTRNN). Our model allows a robot to acquire the representation of a tool function and the required motion without having any prior knowledge of the tool. It is composed of five modules: image feature extraction, body model, tool dynamics feature, tool recognition, and motion recognition. Self-organizing maps (SOM) are used for image feature extraction from raw images. The MTRNN is used for body model learning. Parametric bias (PB) nodes are used to learn tool dynamic features. The PB nodes are attached to the neurons of the MTRNN to modulate the body model. A hierarchical neural network (HNN) is implemented for tool and motion recognition. Experiments were conducted using OpenHRP3, a robotics simulator, with multiple tools. The results show that the tool-body assimilation model is capable of recognizing tools, including those having an unlearned shape, and acquires the required motions accordingly.

Unmanned construction machines are used after disasters. Compared with manned construction, time efficiency is lower because of incomplete visual information, communication delay, and lack of tactile experience. Visual information is the most fundamental items for planning and judgment, however, in current vision systems, even the posture and zoom of cameras are not adjusted. To improve operator's visibility, these parameters must be adjusted in accordance with the work situation. The purpose of this study is thus to analyze effective camera images from some comparison experiments, as a fundamental study of advanced visual support. We first developed a virtual reality simulator to enable experimental conditions to be modified easier. To effectively derive required images, experiments with two different camera positions and systems (fixed cameras and manually controllable cameras) were then conducted. The results indicate that enlarged views to show the manipulator is needed in object grasping and tracking images to show the movement direction of the manipulator is needed in largely end-point movement. The result also confirms that the operational accuracy increases and blind spot rate decreases by using the manual system, com-pared with fixed system.

In the present study, we demonstrate the learning and recognition capabilities of our recently proposed recurrent neural network (RNN) model called stochastic continuous-time RNN (S-CTRNN). S-CTRNN can learn to predict not only the mean but also the variance of the next state of the learning targets. The network parameters consisting of weights, biases, and initial states of context neurons are optimized through maximum likelihood estimation (MLE) using the gradient descent method. First, we clarify the essential difference between the learning capabilities of conventional CTRNN and S-CTRNN by analyzing the results of a numerical experiment in which multiple fluctuating temporal patterns were used as training data, where the variance of the Gaussian noise varied among the patterns. Furthermore, we also show that the trained S-CTRNN can recognize given fluctuating patterns by inferring the initial states that can reproduce the patterns through the same MLE scheme as that used for network training. © 2014 Springer International Publishing Switzerland.

This paper discusses a possible neurodynamic mechanism that enables self-organization of two basic behavioral modes, namely a 'proactive mode' and a 'reactive mode,' and of autonomous switching between these modes depending on the situation. In the proactive mode, actions are generated based on an internal prediction, whereas in the reactive mode actions are generated in response to sensory inputs in unpredictable situations. In order to investigate how these two behavioral modes can be self-organized and how autonomous switching between the two modes can be achieved, we conducted neurorobotics experiments by using our recently developed dynamic neural network model that has a capability to learn to predict time-varying variance of the observable variables. In a set of robot experiments under various conditions, the robot was required to imitate other's movements consisting of alternating predictable and unpredictable patterns. The experimental results showed that the robot controlled by the neural network model was able to proactively imitate predictable patterns and reactively follow unpredictable patterns by autonomously switching its behavioral modes. Our analysis revealed that variance prediction mechanism can lead to self-organization of these abilities with sufficient robustness and generalization capabilities.

We propose a model for robots to use tools without predetermined parameters based on a human cognitive model. Almost all existing studies of robot using tool require predetermined motions and tool features, so the motion patterns are limited and the robots cannot use new tools. Other studies use a full search for new tools
however, this entails an enormous number of calculations. We built a model for tool use based on the phenomenon of tool-body assimilation using the following approach: We used a humanoid robot model to generate random motion, based on human body babbling. These rich motion experiences were then used to train a recurrent neural network for modeling a body image. Tool features were self-organized in the parametric bias modulating the body image according to the used tool. Finally, we designed the neural network for the robot to generate motion only from the target image. © 2014 Springer International Publishing Switzerland.

A method to tune a basic input/output gain (BIOG) according to usage conditions for human-machine systems is proposed for improving work performance. Adapting a BIOG is effective in terms of improving operability and work efficiency, but frequent changes using a gain scheduling strategy degrade the operability by confusing the machine dynamics. The proposed tuning system adjusts a BIOG at long intervals on the basis of comprehensive features in operator and work content obtained from the histogram of control lever input. The target value is set to the normal distribution, meaning that all ranges of the control lever are evenly used in a spring-type lever, as leveling the histogram independent of an operator and work content provides the consistent operational feeling, which leads to comfortable operability. To ensure practicality and effectiveness, a BIOG curve is set to a polygonal line involving a break point and a saturation point that are tuned by equivalent transform of area differences between the obtained histogram and normal distribution curves. Two types of experimental task were performed using a hydraulic arm system. Results showed that the proposed BIOG tuning system improves time efficiency by reducing the area difference while increasing the subjective usability compared with a conventional fixed BIOG system.

This paper investigates the essential difference between two types of behavior generation schemes, namely, sensory reflex behavior generation and intentional proactive behavior generation, by proposing a dynamic neural network model referred to as stochastic multiple-timescale recurrent neural network (S-MTRNN). The proposed model was employed in an experiment involving robots learning to cooperate with others under the condition of potential unpredictability of the others' behaviors. The results of the learning experiment showed that sensory reflex behavior was generated by a self-organizing probabilistic prediction mechanism when the initial sensitivity characteristics in the network dynamics were not utilized in the learning process. In contrast, proactive behavior with a deterministic prediction mechanism was developed when the initial sensitivity was utilized. It was further shown that in situations where unexpected behaviors of others were observed, the behavioral context was re-situated by adaptation of the internal neural dynamics by means of simple sensory reflexes in the former case. In the latter case, the behavioral context was re-situated by error regression of the internal neural activity rather than by sensory reflex. The role of the top-down and bottom-up interactions in dealing with unexpected situations is discussed.

In this work, we developed a contact-type displacement sensor called the magnetic powdery sensor (MPS) that consists of magnets and iron powder. The self-repairing functions of the components of robots, such as wires, sensors, and actuators, have an important role in extending the life of robots. To realize the self-repairing functions of such components, we proposed a magnetic powdery wire (MPW) in our earlier research. The success of this study led us to consider other components of robots where the repairing mechanism could to be applied, leading to the development of the MPS. The key features of the MPS are its simple structure and self-repairing capability. The MPS is implemented with two sets of parallel Acrylonitrile-Butadiene-Styrene (ABS) plates with a magnet fixed at the center. The distance between the plates varies between 7 mm and 23 mm, and their magnetic fields are in the same direction. We implemented the MPS by dispersing iron powder between the plates. The resistance decreases as the distance between the plates reduces. Through experimental results, we confirmed that the displacement can be measured by the MPS and that the MPS has self-repairing capability. As the sensor part of the MPS is composed of iron powder, it can be repaired in the same way as the MPW. These results demonstrate that the life of the magnetic powdery displacement sensor can be extended.

Recognizing grasped objects with tactile sensors is beneficial in many situations, as other sensor information like vision is not always reliable. In this paper, we aim for multimodal object recognition by power grasping of objects with an unknown orientation and position relation to the hand. Few robots have the necessary tactile sensors to reliably recognize objects: in this study the multifingered hand of TWENDY-ONE is used, which has distributed skin sensors covering most of the hand, 6 axis F/T sensors in each fingertip, and provides information about the joint angles. Moreover, the hand is compliant. When using tactile sensors, it is not clear what kinds of features are useful for object recognition. Recently, deep learning has shown promising results. Nevertheless, deep learning has rarely been used in robotics and to our best knowledge never for tactile sensing, probably because it is difficult to gather many samples with tactile sensors. Our results show a clear improvement when using a denoising autoencoder with dropout compared to traditional neural networks. Nevertheless, a higher number of layers did not prove to be beneficial.

Force sensing is a crucial task for robots, especially when the end effectors such as fingers and hands need to interact with an unknown environment, for example in a humanoid robot. In order to sense such forces, a force/torque sensor is an essential component. Many available force/torque sensors are based on strain gauges, but other sensing principles are also possible. In this paper we describe steps towards a capacitive type based sensor. Several MEMS capacitive sensors are described in the literature; however very few larger sensors are available, as capacitive sensors usually have disadvantages such as severe hysteresis and temperature sensitivity. On the other hand, capacitive sensors have the advantage of the availability of small sized chips for sensor readout and digitization. We employ copper beryllium for the transducer, which has been modified from the ones described in the literature to be able to be used in a small sized, robust force/torque sensor. Therefore, as the first step toward the goal of building such a sensor, in this study we have created a prototype sensing unit and have tested its sensitivity. No viscoelastic materials are used for the sensing unit, which usually introduce severe hysteresis in capacitive sensors. We have achieved a high signal-to-noise ratio, high sensitivity and a range of 10 Newton.

A practical framework for measuring the mass of an object grasped by the end-effector of a large-scale hydraulic manipulator, such as construction manipulators, is proposed. Such a measurement system requires high accuracy and robustness considering the nonlinearity and uncertainty in hydraulic pressure-based force measurement. It is thus difficult to precisely model the system behaviors and completely remove error force components, so our framework adopts a less-error data selection approach to improving the reliability of the measurand. It first detects the on-load state to extract reliable data for mass measurement, including evaluating measurement conditions to omit sensors in indeterminate conditions and redefining three-valued outputs such as on, off, or not determinate, to improve robustness, then extracts data during the object-grasp state identified by the grasp motion model and removes high-frequency component by a simple low-pass filter, to improve accuracy, and finally integrates date from plural sensors using the posture-based priority and averages all selected data, to improve reliability. Evaluation experiments were conducted using an instrumented hydraulic arm. Results indicate that our framework can precisely measures the mass of the grasped object in various detection conditions with less errors.

This study proposes a novel type of dynamic neural network model that can learn to extract stochastic or fluctuating structures hidden in time series data. The network learns to predict not only the mean of the next input state, but also its time-dependent variance. The training method is based on maximum likelihood estimation by using the gradient descent method and the likelihood function is expressed as a function of the estimated variance. Regarding the model evaluation, we present numerical experiments in which training data were generated in different ways utilizing Gaussian noise. Our analysis showed that the network can predict the time-dependent variance and the mean and it can also reproduce the target stochastic sequence data by utilizing the estimated variance. Furthermore, it was shown that a humanoid robot using the proposed network can learn to reproduce latent stochastic structures hidden in fluctuating tutoring trajectories. This learning scheme is essential for the acquisition of sensory-guided skilled behavior. © 2013 IEEE.

Focused Assessment with Sonography for Trauma (FAST) is widely used as a first lifesaving step for patients suffering from internal bleeding. Because it may take a long time to transport such patients to a hospital, a wearable and portable tele-echography robot that a paramedic can attach to the patient has been developed. In the current study, experiments were conducted to evaluate the usability and performance of attached FAST.
The proposed robot must be attached to 4 areas to perform FAST. The time required for attachment and the positions of attachment completed by 9 non-medical staff members, as well as the time it took for the FAST to reach a medical doctor, were measured. The echo images obtained when the patient's body was in motion were evaluated by a medical doctor.
The robot could be attached to all 4 areas within approximately 5 min, and the maximum gap was 4.8 cm. This indicates that a paramedic who has received training in emergency medical care should be able to attach the robot to a patient quickly and accurately. Additionally, it was confirmed that the robot could be used to complete FAST under a doctor's control within 9 min and that the extracted echo images were suitable for FAST.
A comparison of the results with current ambulance transportation time confirmed that FAST could be completed approximately 14 min before the patient reached the hospital. The results of the current study indicate that the robot is worth using, is suitable for FAST, and will be effective in emergency medical care. (c) 2012 IPEM. Published by Elsevier Ltd. All rights reserved.

We discuss the recognition of robot's boundaries. Humans think about psychological boundaries of robots in addition to physical ones while interacting with them. We made two hypotheses regarding psychological boundaries. Firstly, these boundaries are affected by the context surrounding the robot. Secondly, these boundaries are controllable. We conducted an experiment with 60 Japanese males and females aged 18-27. Each participant was told a message that 'slide me slightly' from a robot while the participant was interacting with it. We analyzed the participants' actions and questionnaires so as to confirm where of their sight was considered to be 'me'. From this analysis, we confirmed that our hypotheses are both true. © 2013 IEEE.

We discuss the recognition of robot's boundaries. Humans think about psychological boundaries of robots in addition to physical ones while interacting with them. We made two hypotheses regarding psychological boundaries. Firstly, these boundaries are affected by the context surrounding the robot. Secondly, these boundaries are controllable. We conducted an experiment with 60 Japanese males and females aged 18-27. Each participant was told a message that "slide me slightly" from a robot while the participant was interacting with it. We analyzed the participants' actions and questionnaires so as to confirm where of their sight was considered to be "me". From this analysis, we confirmed that our hypotheses are both true.

We discuss the effective implementation of parallel processing for linear prediction-based uniform state sampling (LPUSS). In previous work, we proposed LPUSS as an optimization algorithm for mechanical motions that assures high optimality of the solutions and computational efficiency. In parallel computation, LPUSS requires balanced memory allocation and managed processing timing. In this paper, we propose an effective parallel computing method that assures high optimality and calculation efficiency in parallel processing using GPU processor. We conducted two experiments to validate the proposed method. In the first experiment, we compared single-thread processing for LPUSS and the proposed parallel processing. As a result of this experiment, calculation speed of LPUSS was about 4-20 times faster than that with single-thread CPU. In the second experiment, we applied the proposed method to the optimization of sixtuple inverted pendulum. As a result, the proposed method optimized the motion within 40 minutes. According to our survey, there is no other optimization method that is applicable to higher than quadruple inverted pendulum models with standard constraints.

This paper introduces the design, fabrication and evaluation of the second generation prototype of a magnetic resonance compatible finger rehabilitation robot. It can not only be used as a finger rehabilitation training tool after a stroke, but also to study the brain's recovery process during the rehabilitation therapy (ReT). The mechanical design of the current generation has overcome the disadvantage in the previous version[13], which can't provide precise finger trajectories during flexion and extension motion varying with different finger joints torques. In addition, in order to study the brain activation under different training strategies, three control modes have been developed, compared to only one control mode in the last prototype. The current prototype, like the last version, uses an ultrasonic motor as its actuator to enable the patient to do extension and flexion rehabilitation exercises in two degrees of freedom (DOF) for each finger. Finally, experiments have been carried out to evaluate the performances of this device.

This paper presents our second generation prototype of a magnetic resonance compatible finger rehabilitation robot. It can not only be used as a finger rehabilitation training tool after a stroke, but also to study the brain's recovery process during the rehabilitation therapy (ReT). The finger driving mechanism in current generation is an one degrees of freedom (DOF) driving mechanism, which could overcome the disadvantage that can't provide precise finger trajectories during flexion and extension motion existing in previous version [11]. In addition, this design can also be adjustable to different persons' finger phalanges, as well as the gap between one finger to another can be easily changed. Furthermore, the kinematic analysis of the finger driving mechanism is detailed in this paper; Following, the describes of the control system and a self-motion control mode are presented. Finally, experiments have been carried out to evaluate the performances of this device.

This paper proposes a framework for visualizing comprehensive work tendency using a frequency plot map of the end-point of a manipulator as a fundamental study of work analysis using long-term data for human-operated work machines. A visualization system requires high generality and commonality to enable to extract various characteristics to be extracted on an arbitrary time scale independently of the work machine specifications, work contents and environment, and machine operator. The proposed framework meets this requirement by first creating a two-dimensional end-point plot map with its origin fixed at the yaw joint of the manipulator to deal with arbitrary usage conditions. It then extracts arbitrary characteristics by using hierarchical feature extraction filters, including binary, quantity, and advanced filters, defined by three kinds of essential data: operation, movement, and load. Finally, it quantifies the filtered map by gridding and normalization to visually grasp its frequency distribution. Two experiments in which the work environments and completion time differed were conducted using an instrumented hydraulic arm. Results indicated that the comprehensive work tendency revealed by using the proposed framework corresponds to the actual work results independently of the various conditions. © 2013 IEEE.

In order to achieve the indoor localization with centimeter- to decimeter-level positioning accuracy under the use of the transmitters of "indoor messaging system" (IMES), a method called "real-time kinematic (RTK) Doppler pose estimation" is proposed. In this method, Doppler shifts are produced in the carrier waves transmitted from the IMES transmitters by moving the receiver antennas. The pose (position and orientation) of the receiver is then determined in real-time by using the Doppler shifts, inclination of the receiver, and geometric relation between the receiver antennas and transmitters. To evaluate the proposed method, an experiment with a mobile robot is conducted. The results of the experiment show that the proposed method can achieve a positioning accuracy of about 10 cm.

This paper addresses footstep detection and classification with multiple microphones distributed on the floor. We propose to introduce geometrical features such as position and velocity of a sound source for classification which is estimated by amplitude-based localization. It does not require precise inter-microphone time synchronization unlike a conventional microphone array technique. To classify various types of sound events, we introduce four types of features, i.e., time-domain, spectral and Cepstral features in addition to the geometrical features. We constructed a prototype system for footstep detection and classification based on the proposed ideas with eight microphones aligned in a 2-by-4 grid manner. Preliminary classification experiments showed that classification accuracy for four types of sound sources such as a walking footstep, running footstep, handclap, and utterance maintains over 70% even when the signal-to-noise ratio is low, like 0 dB. We also confirmed two advantages with the proposed footstep detection and classification. One is that the proposed features can be applied to classification of other sound sources besides footsteps. The other is that the use of a multichannel approach further improves noise-robustness by selecting the best microphone among the microphones, and providing geometrical information on a sound source. © 2013 IEEE.

This study shows that a novel type of recurrent neural network model can learn to reproduce fluctuating training sequences by inferring their stochastic structures. The network learns to predict not only the mean of the next input state, but also its time-varying variance. The network is trained through maximum likelihood estimation by utilizing the gradient descent method, and the likelihood function is expressed as a function of both the predicted mean and variance. In a numerical experiment, in order to evaluate the performance of the model, we first tested its ability to reproduce fluctuating training sequences generated by a known dynamical system that were perturbed by Gaussian noise with state-dependent variance. Our analysis showed that the network can reproduce the sequences by predicting the variance correctly. Furthermore, the other experiment showed that a humanoid robot equipped with the network can learn to reproduce fluctuating tutoring sequences by inferring latent stochastic structures hidden in the sequences.

The purpose of this paper is to model and identify an object grasp motion, which is a requisite condition to make a demolition machine grasp an object. Grasping an object is a highly difficult task that requires safe and precise operations, so identifying a grasp or non-grasp is necessary for providing operational support, particularly in disaster response work. These types of outdoor machines lack visual and tactile sensors, so practically available lever operation and cylinder pressure sensors are adopted. The grasp motion is modeled by using sequential transitions of the on-off state of the control signal and cylinder pressure data for the grapple and the manipulator. The results of experiments conducted to transport objects using an instrumented hydraulic arm indicated that the modeled grasp motion effectively identifies a grasp or non-grasp with high accuracy, independently of operators and environments.

Handling objects with a single hand without dropping the object is challenging for a robot. A possible way to aid the motion planning is the prediction of the sensory results of different motions. Sequences of different movements can be performed as an offline simulation, and using the predicted sensory results, it can be evaluated whether the desired goal is achieved. In particular, the task in this paper is to roll a sphere between the fingertips of the dexterous hand of the humanoid robot TWENDY-ONE. First, a forward model for the prediction of the touch state resulting from the in-hand manipulation is developed. As it is difficult to create such a model analytically, the model is obtained through machine learning. To get real world training data, a dataglove is used to control the robot in a master-slave way. The learned model was able to accurately predict the course of the touch state while performing successful and unsuccessful in-hand manipulations. In a second step, it is shown that this simulated sequence of sensor states can be used as input for a stability assessment model. This model can accurately predict whether a grasp is stable or whether it results in dropping the object. In a final step, a more powerful grasp stability evaluator is introduced, which works for our task regardless of the sphere diameter.

This paper investigates a possible neurodynamic mechanism that enables autonomous switching between two basic behavioral modes, namely a "proactive mode" and a "reactive mode." In the proactive mode, actions are generated as intended, whereas in the reactive mode actions are generated in response to the sensory state.We conducted neurorobotics experiments to investigate how these two modes can develop and how a robot can learn to switch autonomously between the two modes as necessary by utilizing our recently developed dynamic neural network model. Tasks designed for the robot included switching between proactive imitation of other's predictable movements using acquired memories and reactive following of other's unpredictable movements through iterative learning of alternating predictable and unpredictable movement patterns. The experimental results revealed that this "meta-learning" capability can lead to self-organization of adequate contextual dynamical structures that can perform autonomous switching between the different behavioral modes. © Springer-Verlag 2013.

This paper proposes a practical framework to estimate whether or not a grapple installed in demolition machines is in a grasp state. Object grasp is a highly difficult task that requires safe and precise operations, so identifying a grasp or non-grasp state is important for assisting an operator. These types of outdoor machines lack visual and tactile sensors, so the proposed framework adopts practically available lever operation and cylinder pressure sensors. The grasp is formed by a grasp motion, which is operations to make the grapple pinch an object, and the grasp state, where the grapple holds the object in any manipulator movements. Thus, the framework determinately confirms the grasp motion through the requisite conditions defined by using sequential changes of binarized operation and pressure data for the grapple and the manipulator, and stochastically confirms the grasp state through the enhancement conditions defined by using force and movement vectors including vertical downward force, movement in the longer direction, and horizontal reciprocating movement. The results of experiments conducted to transport objects using an instrumented hydraulic arm indicated that the proposed framework is effective for identifying grasp/non-grasp with high accuracy, independently of various operators and environments.

In this paper, we discuss a signal transmission function of an artificial circulatory system. We proposed a biomimetic circulatory system so as to imitate the homeostatic function of the human circulation system [1]. The following blood functions were imitated in the system: (1) clotting, (2) energy supply, and (3) motor cooling. However, the circulatory system has other functions, for example, signal transduction by hormones. Thus, we propose adding a signal transmission function to our artificial circulatory system. The proposed function was realized with iron powder and a magnetic field. Particles of iron powder aligned along the direction of the magnetic field lines and formed as a wire. We defined this as a magnetic powdery wire (MPW). The MPW has a self-repairing function. When the wire collapsed, iron powder was transferred to the MPW, and it was repaired. As a result of a signal transmission experiment, we confirmed that the pulse signals were transmitted properly. In the experiment, we observed a time lag in the rise time and fall time on the pulse waveforms. We hypothesized that this was because the wire consisted of iron powder, and the magnetic field became similar to that of a coil. As a result of the self-repairing experiment, we confirmed that the wire repaired itself after being broken and transmitted pulse signals properly. These results demonstrate that our system is able to transmit information and self-repair with iron powders and a magnetic field. Additionally, the observed phenomenon will be the basis for a new device.

This paper describes a tele-operation simulator using a virtual reality (VR) environment for advanced unmanned construction. VR simulators, which can measure arbitrary data, reproduce the same situations, and change the machine and environmental configurations more easily, compared with actual environments, are effective to create tele-operation technologies and quantitatively evaluate them as well as to improve operational skills in complex disaster response works. In this basic study, a VR simulator including a VR environment, operation-input, and video-output components was developed. (i) The VR environment is built using a basic graphic library and dynamics engine. (ii) The operation-input component consists of control levers for a demolition machine with a grapple and environmental cameras with yaw, pitch, and zoom functions. (iii) The video-output component consists of a two-dimensional monitor for displaying an in-vehicle camera view, environmental camera views, and machine status. The results of experiments conducted to transport debris using the VR simulator indicated that the operators adequately completed the debris transport in the VR environment while watching the monitor views from the in-vehicle and environmental cameras.

A pragmatic framework for detecting (identifying the on-off state of) the external force applied to a construction manipulator (front load) by using a hydraulic sensor is proposed. Such a load detecting system requires high accuracy and robustness considering the uncertainty in pressure-based force measurement. The proposed framework first identifies the dominant error force component, including self-weight and driving force, using theoretical and experimental estimation and binarizes the analog cylinder external force. It then evaluates detection conditions to address indeterminate conditions such as stroke-end, singular posture, and impulsive or oscillatory force and redefines three-valued outputs such as on, off, or not determinate (ND). It finally outputs the front load decision by combining all the cylinder decisions to improve robustness through priority analysis. Experiments were conducted using an instrumented hydraulic arm. Results indicate that the proposed framework detects on, off, and ND outputs of the front load more accurately, robustly, and stably in various detection conditions.

This paper introduces a novel neuro-dynamical model that accounts for possible mechanisms of action imitation and learning. It is considered that imitation learning requires at least two classes of generalization. One is generalization over sensory-motor trajectory variances, and the other class is on cognitive level which concerns on more qualitative understanding of compositional actions by own and others which do not necessarily depend on exact trajectories. This paper describes a possible model dealing with these classes of generalization by focusing on the problem of action compositionality. The model was evaluated in the experiments using a small humanoid robot. The robot was trained with a set of different actions concerning object manipulations which can be decomposed into sequences of action primitives. Then the robot was asked to imitate a novel compositional action demonstrated by a human subject which are composed from prior-learned action primitives. The results showed that the novel action can be successfully imitated by decomposing and composing it with the primitives by means of organizing unified intentional representation hosted by mirror neurons even though the trajectory-level appearance is different between the ones of observed and those of self-generated. (C) 2011 Elsevier B.V. All rights reserved.

The purpose of this paper is to propose blood flow measurement algorithms under non-periodic displacement of an artery by controlling an ultrasound (US) probe. Detecting the position and velocity of a bleeding source is required as the first step in treating internal bleeding in emergency medicine. However, the current methods for detecting a bleeding source involve an invasive approach and cannot quantitatively estimate the velocity of bleeding. Therefore, current emergency medical care requires an alternative system to address these problems. In this study, we aim to develop a blood flow measurement system for detecting a bleeding source by using a non-invasive modality, such as a US imaging device. Some problems related to the measurement error still need to be addressed before we can create this system. In particular, the blood flow measurement error in the abdominal area is typically large because the displacement of the artery is too large and non-periodic to adequately control the probe. As the first step in solving these problems, we focused on the displacement of the artery towards the out-of-plane state of a US image and developed measurement algorithms to control the probe under the displacement based on respiratory information. We conducted cross-sectional area and flow rate measurement experiments using an ultrasound phantom containing an artery model and a manipulator equipped with a US probe (BASIS-1). The results represent the first experimental validation of the proposed algorithms. © 2012 IEEE.

We discuss the optimality and computational efficiency of sampling based motion planning (SBMP), which calculates dynamically precise and approximately optimal state transitions using arbitrarily selected nonlinear control system models. To ensure high optimality and computational efficiency, SBMP requires an approximately uniform state sampling function, though the non-linearity of the system models does not allow a perfect function. We propose linear prediction-based uniform state sampling (LPUSS) that samples approximately uniform state points while ensuring a dynamically correct state transition profile with a small calculation cost. LPUSS samples a state by using the given non-linear control system model after determining the input values by using a local linear transition model. We developed a mechanical motion planning system using LPUSS, articulated body algorithm, and parallel computing techniques. To validate LPUSS, we conducted experiments on double, triple, and sixtuple inverted pendulum models. LPUSS showed better optimality and computational efficiency with the double and triple inverted pendulum models, compared with randomized kinodynamic planning (RKP), which is based on rapid random tree (RRT), and our previously proposed rapid semi-optimal motion-planning method in which state sampling is based on uniform inputs. In particular, compared with our previous method, LPUSS was respectively 130 times and 3,000 times faster on double and triple inverted pendulum models under the condition of the same optimality. LPUSS found an approximately optimal swing up motion for the sixtuple inverted pendulum model within 40 minutes. According to our survey, there is no other optimization method that is applicable to higher than quadruple inverted pendulum models with standard constraints.

This paper proposes a novel hybrid-structured model for the adaptive localization of robots combining a stochastic localization model and a rhythmic action model, for avoiding vacant spaces of landmarks efficiently. In regularly arranged landmark environments, robots may not be able to detect any landmarks for a long time during a straight-like movement. Consequently, locally diverse and smooth movement patterns need to be generated to keep the position estimation stable. Conventional approaches aiming at the probabilistic optimization cannot rapidly generate the detailed movement pattern due to a huge computational cost; therefore a simple but diverse movement structure needs to be introduced as an alternative option. We solve this problem by combining a particle filter as the stochastic localization module and the dynamical action model generating a zig-zagging motion. The validation experiments, where virtual-line-tracing tasks are exhibited on a floor-installed RFID environment, show that introducing the proposed rhythm pattern can improve a minimum error boundary and a velocity performance for arbitrary tolerance errors can be improved by the rhythm amplitude adaptation fed back by the localization deviation.

In this paper, we discuss the self-preserving function in the pulsation of circulation systems. Humans and many animals have circulation systems, which send blood with pulsation. However, how the pulsation is supporting the survival capability of them is not studied sufficiently. We propose an artificial circulation system that imitates homeostatic functions of natural blood circulation systems. The proposed system uses mixed fuel solution instead of blood, which is able to exclude the affection of the clotting function of blood platelet. Thus, the proposed system provides easy discussion for the affection of blood pulsation for clotting. As a result of a clotting experiment, we found the phenomena that pulsation helps clotting capability. As a result of energy supply experiment, pulsation helped smooth filtering for pure solution. These results show the importance of pulsation in artificial hearts. Also, the results are applicable to fuel supply systems so as to increase their safety.

This paper presents the design, fabricate and evaluation of a Magnetic Resonance compatible finger rehabilitation device, which could not only be used as a finger rehabilitation training tool after stroke, but also to study the brain's recovery process during the rehabilitation therapy (ReT).
The mechanics of this device are designed to be adjustable to different persons' finger phalanges, and also the gap between one finger to another can be easily changed. By using an ultrasonic motor as its actuator, the device has been designed to be portable, with a high torque output. In addition, the mechanical has been developed into two working models (passive and active) in order to overcome the intrinsic shortage of non-back drivability in ultrasonic motor. The result system enables the client to do extension and flexion rehabilitation exercises in two degrees of freedom (DOF) for each finger as well as one DOF motion on the thumb. Finally, experiment has been carried out to evaluate the performance of the device.

A Doppler positioning method with orientation estimation for an indoor messaging system (IMES) is proposed. With this method, both position and orientation of a receiver are estimated simultaneously by using Doppler shifts produced by moving a receiver antenna under the use of two or more IMES transmitters. The proposed method is evaluated through an experiment in which the interval of two transmitters is varied. The results of the experiment demonstrate that centimeter- to decimeter-level positioning accuracy and orientation-estimation accuracy of +/- 3 degrees are achieved; these results were largely consistent with the theoretical values calculated from dilution of precision. In addition, magnetic-compass error indoors was experimentally investigated; the results show that a magnetic-compass is a large error source if it is used indoors. Lastly, which initial values of a nonlinear least-square used for the proposed method converge to appropriate position and orientation solutions is analyzed; the results of the analysis suggest that if an initial position is set to the midmost of two transmitters, a proper solution is obtained except in the case that initial orientation is 180 degrees opposite from the correct orientation.

A new technique was developed to solve inverse kinematics based on quadratic minimization. Firstly the inverse kinematic problem was formulated as a quadratic minimization problem through the constitution of a quadratic objective function derived from quaternion based kinematic description, then a new technique is developed to solve the quadratic minimization problem. The iteration procedures of the new technique are organized in two main phases: compute the search direction based on dynamic system synthesis; seek for acceptable step along the search direction. Numerical examples showed that inverse kinematic solutions can be delivered in a robust and time-efficient way, to meet the requirements coming across in the application field of every daily living support.

In this paper, we address the autonomous evidence accumulation when a system classifies an object into one of predetermined categories. We propose a reinforcement learning system that effectively selects actions to speed up the classification process. The proposed system accelerates its learning using classification probabilities calculated by a classification system. We conducted three binary classification experiments to evaluate the learning speed and correctness of the proposed system. In the first experiment, we examined a random action selection strategy that does not learn its selection parameters while accumulating evidence. In the second experiment, we examined Paletta's reinforcement learning system that observes the state of the object and learns action selection strategy. In the third experiment, we examined the proposed system that observes both the object state and the classification probability. The proposed system showed the fastest learning.

A novel positioning method based on the difference between phases of carrier waves transmitted from closely located pseudolite antennas is proposed. This method can avoid the major problems with the conventional positioning method using pseudolites. A three-channel pseudolite applying the proposed method was developed, and its positioning performance was experimentally evaluated. In the experiment, carrier-phase differences were measured at thirty-three points in a field of 4 x 4 m; their two-dimensional positions were estimated. The experimental measurements show that the method can achieve decimeter- to meter-level positioning accuracy. They also show that many factors, such as dilution of precision, carrier-to-noise density ratio, multipath propagation, and phase-center variation of antennas, affect the positioning performance.

A new interfacing device that enables intuitive operation of a wheelchair mounted robotic arm is proposed. Three kinds of physical signals: jaw movement, breathing strength and head movement are captured using this device, and then be used to drive motion primitives of robotic arm in an intuitive way. This device is designed to operate in a mouth-only manner, thus enables usage for the upper limb disabled. Experiments are also conducted making use of this device and wheelchair mounted robotic arm to complete tasks in activities of daily living.

This paper describes an operational support system to simplify removing objects from a building structure in demolition work using dual-arm construction machinery. An operator cannot recognize the manipulator movement visually when a target object is strongly restrained in an unmovable environment, so the operator continues large lever-operations for a while after the object has been removed. This could lead to a collision against the surrounding environment. An operational support in construction machinery filed requires the compatibility among efficiency, quality, and safety and the commonality among various operator skill levels. The proposed support system thus identifies the moment when an object has been removed and cancels any operational inputs to ensure safety work when generating intensive force applied to a manipulator, focusing only on the common physical phenomena. An object-removal flag is defined as a situation where force applied to the end-point of a manipulator decrease rapidly in which lever operation and hand load are detected. Demolition experiments are conducted using a hydraulic dual arm system. Result indicates that the displacement of end-effector after removal and the number of error contacts are decreased while decreasing time consumption.

In this paper, a quantitative analysis method for a comprehensive work flow in construction work for identifying work states in more detail is proposed. The proposed method is based on analyzing state transitions of simplified primitive static states (s-PSS), which consist of four symbolic work states defined by using the on-off state of lever operations and manipulator loads. First, practical state transitions (PST), which are common and frequent transitions in arbitrary construction work, are defined on the basis of the transition rules, according to which an operation flag changes arbitrarily and a load flag changes only during a lever operation. Second, PST is notionally classified into essential (EST) and nonessential (NST) state transitions whose definitions change depending on the task phase, including reaching, contacting, loadworking, and releasing. Third, closed loops formed by EST represent work content and those formed by NST represent wasted movements. In work-analysis experiments using our instrumented setup, results indicated that all s-PSS definitely changes on the basis of PST under various experimental conditions and that work analysis using EST and NST easily reveals untrained tasks related to wasted movements.

This study investigated the potential for autonomous harvesting of strawberries cultivated in a hanging-type elevated substrate.<BR> Investigation of degree of overlap between inside and outside revealed that approximately 90% of mature fruits are located at the front and can be detected by an inside inspection. Harvesting tests were conducted using an inside-approach harvesting robot that can travel beneath a hanging bench and pick fruits on both sides using a single manipulator. The success rates for detecting and for picking were 68.7% and 50.6% for var. Benihoppe, and 75.0% and 63.0% for var. Amaotome. Our investigation of degree of overlap and harvesting tests demonstrated that the improved visibility of mature fruits from inside led to significantly enhanced picking performance.

Studying the brain's recovery process after a stroke during the rehabilitation therapy (ReT) can help to provide important information leading to new rehabilitation treatment and also may help physiotherapists to tailor an optimized training plan for each patient. However, there are few devices that could be used as daily training rehabilitation tool and also can be used for studying the brain activity during ReT. Therefore, in our research, we developed an MRI compatible robot for finger rehabilitation, which possesses the required functions both. The mechanics of this robot were designed to be adjustable to different persons' finger phalanges, and also the gap between one finger to another can be easily changed. By using an ultrasonic motor as its actuator, the device has been designed to be portable, with a high torque output. It enables the client to do extension and flexion rehabilitation exercises in two degrees of freedom (DOF) for each finger. Finally, experiments have been carried out to evaluate the performance of the device. © 2012 IEEE.

This paper addresses a phase space partitioning problem in motion planning systems. In a previous study, we developed a kinematic and dynamic motion planning system, known as rapid semi-optimal motion-planning (RASMO), that ensures the optimality of the planned motions with rapid calculations using a partition for the phase space. The shape of the partition determines the optimality of the motion. We propose a state-dispersion-based phase space partitioning (SDPP) method that generates adaptive partitions for RASMO and the same class of motion planning systems. These partitions allow motion planning systems to plan motions with better optimality. To validate SDPP method, we compared the optimality of RASMO in several conditions using a double inverted pendulum model while setting the optimality criterion of RASMO to time. Results show that RASMO with SDPP planned smaller time motions than that obtained RASMO with a uniform partition. Once this method is applied to a machine (e.g. industrial or space robots), the planning system provides better motions with the same calculation cost.

We address the applicability of nonlinear model predictive control (NMPC) to rigid bodies with multi degrees of freedom. These systems have complicated nonlinear motion equations that cause large computational time for NMPC.We propose an asymptotically optimal NMPC core based on the uniform state sampling concept. We examined the optimality and the computational cost of the proposed core using double and triple inverted pendulum models. The result showed that the proposed core calculates sub-time-optimal motions with 100 and 1,000 times faster than the competing cores with maintaining the same optimality. We applied the proposed core to a sixth inverted pendulum model. The result showed that the proposed NMPC core is able to calculate a sub-time-optimal motion of the model. © 2012 IFAC.

As fundamental research for human-robot interaction, this paper addresses the rhythmic reference of a human while turning a rope with another human. We hypothyzed that when interpreting rhythm cues to make a rhythm reference, humans will use auditory and force rhythms more than visual ones. We examined 21-23 years old test subjects. We masked perception of each test subject using 3 kinds of masks, an eye-mask, headphones, and a force mask. The force mask is composed of a robot arm and a remote controller. These instruments allow a test subject to turn a rope without feeling force from the rope. In the first experiment, each test subject interacted with an operator that turned a rope with a constant rhythm. 8 experiments were conducted for each test subject that wore combinations of masks. We measured the angular velocity of force between a test subject/the operator and a rope. We calculated error between the angular velocities of the force directions, and validated the error. In the second experiment, two test subjects interacted with each other. 1.6 - 2.4 Hz auditory rhythm was presented from headphones so as to inform target turning frequency. Addition to the auditory rhythm, the test subjects wore eye-masks. The first experiment showed that visual rhythm has little influence on rope-turning cooperation between humans. The second experiment provided firmer evidence for the same hypothesis because humans neglected their visual rhythms.

This paper proposes an organ boundary determination method for detecting internal bleeding. Focused assessment with sonography for trauma (FAST) is important for patients who are sent into shock by internal bleeding. However, the FAST has a low sensitivity, approximately 42.7 %, and delays of lifesaving treatment due to internal bleeding being missed have become a serious problem in emergency medical care. This study aims, therefore, to construct an automatic internal bleeding detection robotic system on the basis of ultrasound (US) image processing to improve the sensitivity. Internal bleeding has two key features: it is extracted from low-brightness areas in US images and accumulates between organs. We developed method for extracting low-brightness areas and determining algorithms of organ boundaries by low-brightness set analysis, and we detect internal bleeding by combining these two methods.
Experimental results based on clinical US images of internal bleeding between Liver and Kidney showed that proposed algorithms had a sensitivity of 77.8% and specificity of 95.7%.

The purpose of this paper is to propose noninvasive internal bleeding detection method by using ultrasound (US) image processing under US cross-section image. In this study, we have developed a robotic system for detecting internal bleeding based on the blood flow measured by using a noninvasive modality like an US imaging device. Some problems related to the measurement error, however, still need to be addressed.
In this paper, we focused on US image processing under US cross-section image, and constructed blood flow measurement algorithm under US cross-section image for internal bleeding detection. We conducted preliminary blood flow measurement experiments using a phantom containing artery model and a manipulator equipped with a US probe (BASIS-1). The results present the experimental validation of the proposed method.

The purpose of this paper is to propose an organ boundary determination method for detecting internal bleeding. Focused assessment with sonography for trauma (FAST) is important for patients who are sent into shock by internal bleeding. However, the FAST has a low sensitivity, approximately 42.7 %. This study aims, therefore, to construct an automatic internal bleeding detection robotic system on the basis of ultrasound (US) image processing to improve the sensitivity. We developed method for determining algorithms of organ boundaries by low-brightness set analysis, and we detected internal bleeding by the proposed method.
Experimental results based on clinical US images of internal bleeding between Liver and Kidney showed that proposed algorithms had a sensitivity of 77.8% and specificity of 95.7%.

The purpose of this paper is to propose blood flow measurement algorithms under non-periodic displacement of an artery by controlling an ultrasound (US) probe. Detecting the position and velocity of a bleeding source is required as the first step in treating internal bleeding in emergency medicine. However, the current methods for detecting a bleeding source involve an invasive approach and cannot quantitatively estimate the velocity of bleeding. Therefore, current emergency medical care requires an alternative system to address these problems.
In this study, we aim to develop a blood flow measurement system for detecting a bleeding source by using a non-invasive modality, such as a US imaging device. Some problems related to the measurement error still need to be addressed before we can create this system. In particular, the blood flow measurement error in the abdominal area is typically large because the displacement of the artery is too large and non-periodic to adequately control the probe. As the first step in solving these problems, we focused on the displacement of the artery towards the out-of-plane state of a US image and developed measurement algorithms to control the probe under the displacement based on respiratory information. We conducted cross-sectional area and flow rate measurement experiments using an ultrasound phantom containing an artery model and a manipulator equipped with a US probe (BASIS-1). The results represent the first experimental validation of the proposed algorithms.

The difficulty of applying "pseudolites" (i.e., pseudo-satellites) and the "indoor messaging system" (IMES) to indoor robot navigation is investigated. Moreover, as an alternative to these methods, "high-accuracy IMES using real-time kinematic Doppler positioning" is introduced and experimentally evaluated. The evaluation showed that this method can achieve centimeter- to decimeter-level positioning accuracy. This result implies the feasibility of indoor-outdoor seamless robot navigation with a GPS/IMES receiver.

Two active-localization methods for mobile robots, namely, "rhythm-based adaptive localization" for floor-embedded RFID tags and "real-time kinematic Doppler positioning" for indoor GPS (an indoor messaging system, or IMES), are proposed. Both methods increase the positioning accuracy of a robot by moving the sensor attached to the robot. The results of a positioning experiment with floor-embedded RFID tags show that the rhythm-based adaptive-localization method can achieve a positioning accuracy of 25 cm under the conditions that noise is intentionally added to the value of a wheel encoder, the installation interval of tags is 30 cm, and the tag installation is incomplete (i.e., some tags are missing). On the other hand, the results of a positioning experiment using IMES show that the real-time kinematic Doppler-positioning method can achieve centimeter to decimeter-level positioning accuracy even if the number of visible transmitters is only one (i.e., trilateration is not used). © 2012 IEEE.

This paper proposes a quantification method for a comprehensive work flow in construction work for describing work states in more detail on the basis of analyzing state transitions of primitive static states (PSS), which consist of 16 symbolic work states defined by using on-off state of the lever operations and joint loads for the manipulator and end-effector. On the basis of the state transition rules derived from a transition-condition analysis, practical state transitions (PST), which are common and frequent transitions in arbitrary construction work, are defined. PST can be classified into essential (EST) or nonessential state transitions (NST). EST extracts common phases of work progress and estimates positional relations between a manipulator and an object. NST reveals wasted movements that degrade the efficiency and quality of work. To evaluate comprehensive work flows modeled by combing EST and NST, work-analysis experiments using our instrumented setup were conducted. Results indicate that all the PSS definitely changes on the basis of PST under various work conditions, and work analysis using EST and NST easily reveals work characteristics and untrained tasks related to wasted movements.

This paper gives an overview of OpenVR, a software tool that is able to construct a virtual robot and its working environment from CAD source files. The virtual robot can be played with the same as a real robot by providing corresponding controller and sensor interfaces, thus contribute to the research of robotics in concept visualization, robot design, and algorithm and software development. General purposed algorithms are also given to simplify the effect for the execution of complex manipulation tasks. © 2011 IEEE.

This paper addresses optimal motion for general machines. Approximation for optimal motion needs a global path planning algorithm that precisely calculates the whole dynamics of a machine in a brief calculation. We propose a path planning algorithm that is composed of a path searching algorithm and a pruning algorithm. The pruning algorithm is based on our analysis for the resemblances of states. To confirm the precision, calculation cost, optimality, and applicability of the proposed algorithm, we conducted several shortest time path planning examinations for the dynamic models of double inverted pendulums. The precision to reach the goal state of the pendulums was better than other algorithms. The calculation was at least 58 times faster. There was a positive correlation between the optimality and the resolutions of the proposed algorithm. As a result of torque based feedback control simulation, we confirmed applicability of the proposed algorithm under noisy situation.

This paper addresses the rhythmic reference in physical human-robot interaction. Human refers to a rhythm from multiple sensing modalities when turning a rope with another human synchronously. This study verifies a hypothesis that some humans mix several rhythms of the modalities into a rhythm (rhythmic reference). Six participants, four males and two females, 21-23 years old, took part in eight experiments which examined the hypothesis. In each experiment, we masked the perception of each participant using eight combination of three kinds of masks, an eye-mask, headphones, and a force mask. Each participant interacted with an operator that turned a rope with a constant frequency. As a result of the experiments, a participant increased the controlling error as the number of masks was increased regardless the types of masked modalities. The result strongly supported our hypothesis.

A significant discussion is provided on the configuration of reader antennas (RAs) affecting a robot's positioning performance in a floor-installed RFID infrastructure for two-wheeled robots. RFID systems where IC tags are installed under/on floors have been widely utilized for positioning infrastructures. The RAs should be properly placed on a robot so that such an environment can give full play to its potential capabilities of positioning the robot. This problem calls for guidelines in designing the configuration of RAs. Our previous simulation study extracted an effect involving RA configuration in front of and behind the robot's moving direction (the forward-backward configuration effect), which can be a critical parameter for positioning accuracy, while it lacked a full investigation of the cause of the effect. This paper not only discusses an extraction of the effect, but also presents a detailed discussion on how it occurs through data analysis, a mathematically schematic proof and a control experiment for validation. Our discussions are widely applicable to localization methods using landmarks on/under the floor because our model assumes a simple and general detecting condition. (C) Koninklijke Brill NV, Leiden and The Robotics Society of Japan, 2011

This paper proposes an analysis method for a comprehensive work flow in construction work for identifying work states in more detail on the basis of analyzing state transitions of simplified primitive static states (s-PSS), which consist of four symbolic work states defined by using on-off state of the lever operations and manipulator loads. First, practical state transitions (PST), which are common and frequent transitions in arbitrary construction work, can be defined on the basis of the transition rules, according to which an operation flag changes arbitrary and load flag only changes during a lever operation. Second, PST can be classified into essential (EST) or nonessential state transitions (NST), and NST changes its definition depending on the task phase. Third, EST and NST represent work contents and wasted movements, respectively, so work-analysis experiments using our instrumented setup were conducted. Results indicate that all the s-PSS definitely changes on the basis of PST under various experimental conditions and that work analysis using EST and NST easily reveals untrained tasks related to wasted movements. © 2011 IEEE.

This paper proposes a practical framework for detecting (identifying the on-off state of) the external force applied to a construction manipulator (front load) by using a hydraulic sensor. Such a detection system requires high accuracy and robustness considering the uncertainty in pressure-based force measurement. Our framework is thus organized into (i) identifying the dominant error force component (self-weight and driving force) using theoretical and experimental estimation and binarizing the analog external cylinder force, (ii) evaluating detection conditions to address indeterminate conditions such as stroke-end, singular posture, and impulsive or oscillatory force and redefining three-valued outputs such as on, off, or not determinate (ND), and (iii) outputting the front load decision by combining all the cylinder decisions to improve robustness through priority analysis. Experiments were conducted using an instrumented hydraulic arm. Results indicate that our framework adequately detects on, off, and ND outputs of the front load in various detection conditions without misidentification. © 2011 IEEE.

This paper proposes a practical framework for measuring the external force applied to a construction manipulator (front load vector) by using a hydraulic sensor. Such a force measurement system requires high accuracy and robustness considering the uncertainty in the construction machinery field, but it inevitably includes measurement errors owing to difficult-to-reduce modeling errors. Our framework thus adopts a relative accuracy improvement strategy without correcting the models for the practicality. It comprises (i) quantifying the internal error range (IER) by using the sum of the maximal measurement errors of static and dynamic friction forces, which change in postural and kinematic conditions, (ii) calculating the error force vector by using IER to select cylinders (sensors) that have less error, and (iii) outputting the front load vector using the cylinders whose error force vector is minimum. Experiments were conducted using an instrumented hydraulic arm. The results indicate that our framework can enhance the relative accuracy of external force measurement independently of various postural and kinematic conditions.

Pseudolites are one of the most promising technologies for indoor localization because they can directly use off-the-shelf GPS/GNSS receivers with minor change of their firmware. However, pseudolites have some fundamental issues such as near-far, multipath, and synchronization problems. Many of these issues derive from using more than one transmitter and trilateration. In this paper, we propose a novel indoor positioning method using a movable receiver antenna and a single pseudolite. In this method, the position is determined by using the Doppler shift produced by a moving antenna. We conducted an experiment by testing different conditions of the moving antenna. The results show that our method has the potential to achieve the positioning with decimeter level accuracy.

A method to improve positioning accuracy of an indoor messaging system (IMES) was developed. This method uses Doppler shifts (produced by moving a receiver antenna) and three-dimensional attitude to determine the position of the receiver. A rotation-type Doppler-measurement system applying this method was developed. To evaluate the system, two experiments were conducted: in one, rotation radius of a movable receiver antenna was varied; in the other, position where positioning is conducted was varied. The experimental results show that the method can achieve centimeter- to decimeter-level positioning accuracy.

Rope turning tasks are useful to explore rhythmic physical human-robot interaction. However, in traditional studies, a robot was not able to turn a rope by itself, because simultaneous control of three factors, i.e., energy transmission, rotational axis and centrifugal force, is difficult when a robot rotates a flexible object such as a rope. In this paper, we propose a method to control these three factors simultaneously. We developed the method by adding a compensator to an attractor that attracts the end-effector of a robot to a uniform circular motion within a fixed radius. In a rope turning simulation, the end-effector of a robot and the center of mass of a simplified rope converged to uniform circular motions. In addition, we applied the proposed method to a rope turning task performed by a humanoid robot. The robot was able to turn a rope with one fixed end or in cooperation with a human. (C) Koninklijke Brill NV, Leiden and The Robotics Society of Japan, 2011

This paper addresses optimal motion for general machines. Approximation for optimal motion requires a global path planning algorithm that precisely calculates the whole dynamics of a machine in a brief calculation. We propose a path planning algorithm that consists of path searching and pruning algorithms. The pruning algorithmis based on our analysis of state resemblance in general phase space. To confirm precision, calculation cost, optimality and applicability of the proposed algorithm, we conducted several shortest time path planning experiments for the dynamic models of double inverted pendulums. Precision to reach the goal states of the pendulums was better than other algorithms. Calculation cost was 58 times faster at least. We could tune optimality of proposed algorithm via resolution parameters. A positive correlation between optimality and resolutions was confirmed. Applicability was confirmed in a torque based position and velocity feedback control simulation. As a result of this simulation, the double inverted pendulums tracked planned motion under noise while keeping within torque limitations.

This paper presents a general method for creating an approximately optimal online stabilization system. An optimal stabilization system is an ideal online system that can calculate each optimal motion leading to a stable mechanical goal state depending on the current state. We propose a system that selects each semi-optimal motion according to the current state from a reverse-time tree. To create the reverse-time tree, we applied rapid semi-optimal motion planning method (RASMO) to a reverse-time search from a stable state. We also developed an online motion selection technique. To validate the proposed method, we simulated the stabilization of a double inverted pendulum. When we used an optimization criteria, time optimal, the system quickly stabilized the pendulum's posture and velocity. When we used higher resolution RASMO, the time approached the optimal time. The general framework proposed here is applicable to a variety of machines.

This study aims to construct a handling and grasp control method by multi-fingered robot hand with soft skin for the precision operation of cylindrical tools. We believe that the key to the improvement of the accuracy of the tool operation is to use an additional grasping point to reduce the tool's posture fluctuation derived from the external force exerted at the tool nib. We focus two factors that cause the tool's posture fluctuation. One is the rotation movement of the tool around the axis between two contacts by finger-tips. The other one is the deflection of the soft skin. We propose the effective allocation of the addition grasping point from the view point of the reduction of the tool's posture fluctuation. Our control architecture is a customized combination of resolved motion rate control and hybrid control which maintains stable grasping and handles the tool along with the desired trajectory of the tool nib. It is validated through the physical tests using the actual multi-fingered robot hand that the accuracy of the tool nib trajectory is improved by proposed control method. © 2011 IEEE.

We propose a novel navigation system for adaptively exploring an obstacle space using diverse ways of touching an object. Conventional navigation models are typically based on the avoidance of obstacles, i.e., avoiding collision. However, actual disordered space may be full of various kinds of obstacles. To reach a destination in such a space, a robot requires an active approach for avoiding a deadlock with obstacles or changing the obstacle configuration to find an open space using diverse ways of touching an object. We solved this problem by generating locally diverse moving patterns by using an action model with rhythmical oscillation in addition to a localization model using a particle filter. The proposed model was demonstrated to be effective through an experiment where a robot navigated to a destination behind partially movable obstacles using rhythmical active touch. © 2011 IEEE.

Robotic technology can be used to effectively regain the previous dexterity of stroke patients in clinical therapy. At the same time, brain images taken with MRI (Magnetic Resonance Imaging) are important for new rehabilitation treatments, and therefore it is useful to develop MRI compatible robot. With these ideas in mind, a novel, MRI compatible finger rehabilitation device was created (Fig.1.). It was also designed to be usable by many different people: it can be adjusted to different finger knuckles, and also the gap between one finger to another can be easily changed. By using an ultrasonic motor as its actuator, the device has been designed to be portable, with a high torque output. It enables the client to do extension and flexion rehabilitation exercises in two degrees of freedom (DOF) for each finger. Finally, several experiments have been carried out to evaluate the performance of the device. © 2011 IEEE.

This paper shows the focused assessment with sonography for trauma (FAST) performance of a wearable tele-echography robot system we have developed that we call "FASTele". FAST is a first-step way of assessing the injury severity of patients suffering from internal bleeding who may be some time away from hospital treatment. So far, we have only verified our system's effectiveness under constantly wired network conditions. To determine its FAST performance within an emergency vehicle, we extended it to a WiMAX mobile network and performed experiments on it. Experiment results showed that paramedics could attach the system to FAST areas on a patient's body on the basis of the attaching position and procedure. We also assessed echo images to confirm that the system is able to extract the echo images required for FAST under maximum vehicle acceleration.

The purpose of this paper is to propose ultrasound visual servoing algorithms for controlling a robotic system equipped with an ultrasound probe for large pulsation and a displacement towards the out-of-plane of a US image.
In this study, we aim to develop a robotic system for detecting bleeding source based on the blood flow measured by using a non-invasive modality like an ultrasound (US) imaging device. Some problems related to the measurement error still need to be addressed. As the first step in solving these problems, we focused on the large pulse amplitude and displacement of the artery towards the out-of-plane of a US image, and developed US visual servoing algorithms to control the probe. We conducted preliminary blood flow measurement experiments using an phantom containing artery model and a manipulator equipped with a US probe (BASIS-1). The results present the first experimental validation of the proposed algorithms.

The purpose of this paper is to propose measurement algorithms of cross-section area and blood speed of an artery by controlling a robotic system equipped with an ultrasound probe for large pulsation and a displacement towards the out-of-plane of a US image. Detecting the position and speed of a bleeding source is required as the first step in treating internal bleeding in emergency medical medicine. However, the current methods for detecting a bleeding source involve an invasive approach and cannot quantitatively estimate the speed of bleeding. Therefore, current emergency medical care requires an alternative system to address these problems. In this study, we aim to develop a robotic system for detecting bleeding source based on the blood flow measured by using a noninvasive modality like an ultrasound (US) imaging device. Some problems related to the measurement error still need to be addressed before we can create this system. In particular, the blood flow measurement error in the abdominal area is typically large, because the pulse amplitude and displacement of the artery is too large even to adequately control the probe. As the first step in solving these problems, we focused on the large pulse amplitude and displacement of the artery towards the out-of-plane of a US image, and developed measurement algorithms to control the probe. We conducted flow volume measurement experiments using an ultrasound phantom containing artery model and a manipulator equipped with a US probe (BASIS-1). The results present the first experimental validation of the proposed algorithms. © 2011 IEEE.

The purpose of this paper is to develop a fundamental external force detection framework for construction manipulator. Such industrial application demands practicality that satisfies detection requirements such as accuracy and robustness while ensuring (i) low cost, (ii) wide applicability, and (iii) simple detection algorithm. To satisfy (i) and (ii), our framework adopts hydraulic sensors as force sensor. Hydraulic sensor essentially detects error force components that generate depending on joint kinetic state and differ in identification- difficulty due to nonlinear and uncertain hydromechanical system. To satisfy (ii) and (iii), theoretical, experimental, and conditional identification methods without complex modeling are applied in static, uniform motion, and accelerated motion states for identifying self-weight, driving, inertial forces defined as dominant error components. Experiments were conducted using our instrumented hydraulic arm system. Result of no-load task indicates that our framework lowers a threshold to determine the on-off state of external force application, independent of joint kinetic states. Result of on-load task confirms that our framework robustly identifies off states in which external force is not applied to hydraulic cylinder.

Pseudolite is expected to be one of the best solutions for indoor positioning because of its compatibility with GPS and its positioning accuracy. However, since cycle slips frequently occur indoors and it is difficult to predict their occurrence, it is not so easy to retain a sufficient number of observation equations for position calculation. To avoid this problem, there are two possible methods
increasing transmitters, which is known as an effective way for outdoor GPS, and increasing receivers. In this paper, we evaluate the performance of these two methods for indoor positioning in terms of positioning success rate and positioning accuracy. The experiment we conducted shows that increasing the number of receivers is more effective than increasing the number of transmitters. © 2010 SICE.

This paper introduces a model for associative learning combining both linguistic and behavior modalities. The model consists of language and behavior modules both implemented by a hierarchical dynamic network model and interacting densely through hub-like neurons, the so-called parametric biases (PB). By implementing this model for a humanoid robot with the task of manipulating multiple objects, the robot was tutored to associate sentences of two different grammatical types with corresponding sensory-motor schemata. The first type was a verb followed by an objective noun such as "hold red" or "hit blue"; the second was a verb followed by an objective noun and further followed by an adverb phrase such as "Put red on blue". Our analysis of the results of a learning experiment showed that two clusters corresponding to these two types of grammatical sentences appear in the PB activity space, such that a specific micro structure is organized for each cluster.

Human-symbiotic humanoid robots that can perform tasks dexterously using their hands are needed in our homes, welfare facilities, and other places as the average age of the population increases. To improve the task performance of human-symbiotic humanoid robots, a motion-planning method with active body-environment contact was developed. Taking into account the positive and negative effect of mechanical passive elements implemented in joints, this motion-planning method can enables the hand-arm system to establish the active BE contact at the appropriate body-site and to select the joints that perform the movement for executing the given task. Control algorithms for the tool operation, namely, writing with a pen, were also constructed. The motion-planning method was validated through actual experiments on a prototype human-symbiotic humanoid robot.

A methodology for setting the reference value of a grasping force when a multi-finger robotic hand grasps and lifts up an object without knowing its characteristic weight, coefficient of static friction, and stiffness was devised. The grasping force must be set to avoid dropping or deforming the object. To fulfill this requirement, the methodology measures object characteristics by detecting the moment of deformation or slipping according to the deflection of a mechanical passive element. The reference value of grasping force is set according to an upper limit to avoid crushing and a lower limit for lifting up the object calculated from the object's above-mentioned characteristics. The degree of the accuracy of object characteristic measurements was evaluated through experiments using an actual human-mimetic hand-arm system. Finally we validated that the system can pick up objects with the grasping force set by our methodology. ©2010 IEEE.

The purpose of this report is to propose a diagnosis and treatment scenario by assistance of bystander and echography robot for trauma patient. Quick treatment is important for patients who have shock by internal bleeding. Therefore, focused assessment with sonography for trauma (FAST), which is a simple and quick diagnostic method, was developed as a first lifesaving step in a hospital. However, a shock patient has little time, and transportation to a hospital may take too long. Therefore, we aim at development of a system which enables FAST at injury scene by assistance of bystander. To develop the system, life-saving flow and a FAST device are significant issues. First, we constructed a diagnosis and treatment scenario. Then, we developed a tele-echography robot system which has 4-DOF that a bystander could attach. This robot is attached to each roughly FAST areas of patient body by a bystander and remotely fine-tuned position by a doctor in a hospital. In this way, a bystander may not do an exact positioning. In addition, the robot has a mechanism to generate contact force between echo probe and patient body surface by two springs. This mechanism not only fit in patient body motion but also reducing the number of controlled axis. To confirm the medical applications of the scenario and the robot, we performed experiments with some examinees and doctors. We confirmed effectiveness of the mechanism and that a bystander could attach the robot to each roughly FAST areas of patient body. We also confirmed that a doctor could do FAST with the robot by remote-controlled on the roughly FAST areas in approximately three minutes. These results show that the robot would enable FAST by assistance of bystander, and the scenario would make FAST faster than the time required transporting the patient to the hospital. ©2010 IEEE.

The purpose of this report is to propose portable and attachable tele-echography robot system: FASTele. Focused assessment with sonography for trauma (FAST) is important for patients who have shock by internal bleeding. However, the patient has little time, and transportation to a hospital may take too long. A system which enables FAST more quickly is required. Therefore, we aim to develop a tele-echography (FAST) robot system that can be used by a paramedic easily for shock patient in ambulance or at injury scene. To develop the system, portability and usability (for paramedic) are significant issues. We developed a tele-echography robot system which has 4-DOF. The robot is attached to each roughly FAST areas of patient body (body-based set up) and remotely fine-tuned position by a specialist in a hospital. The robot can control the posture of probe by curvature rails. The mechanism that maintains passively the contact force between the probe and patient&apos;s body surface by using springs enables the robot small and lightweight. Feasibility experiments of FAST are reported.

This paper reports a wearable tele-echography robot that a bystander could attach to a patient at injury scene. Quick diagnosis and treatment are important for patients who have shock by internal bleeding. Therefore, focused assessment with sonography for trauma (FAST), which is a simple and quick diagnostic method, was developed as a first lifesaving step in a hospital. However, a shock patient has little time, and transportation to a hospital may take too long. Therefore, a system which enables FAST at injury scene by assistance of bystander is important. First, we constructed a medical treatment scenario from the victim's discovery to FAST and treatment at the injury scene. Then, we developed a remote-controlled FAST robot that a bystander could attach to a patient. This robot is attached to each roughly FAST areas of patient body by a bystander and remotely fine-tuned position by a doctor in a hospital. In this way, a bystander may not do an exact positioning. In addition, the robot has two springs to generate contact force between echo probe and patient body surface. This mechanism not only fit in patient body motion but also downsizing based on reducing the number of controlled axis. To confirm the effectiveness of the robot, we performed experiments with some examinees and doctors. We confirmed effectiveness of the mechanism and that a bystander could attach the robot to each roughly FAST areas of patient body. We also confirmed that a doctor could do FAST with the robot by remote-controlled on the roughly FAST areas in approximately three minutes. These results show that the robot would enable FAST at injury scene by assistance of bystander, and FAST would be faster than the time required transporting the patient to the hospital with the robot. This is effective in improving the survival rate for traumatic shock patients. © 2010 IEEE.

Reader antennas' configuration for two wheeled robots were evaluated to estimate their posture from floor-installed RFID tags. RFID systems where IC tags are installed under/on floors have widely been utilized in recent years as the next positioning infrastructure. The reader antennas should be properly placed on a robot so that such an environment can give full play to its potential capabilities of positioning the robot. This problem calls for guidelines in designing the configuration of reader antennas. Experiments using actual robots cannot offer sufficient data because of time and physical limitations, which prevent helpful and reproducible evaluations. We overcame this problem by constructing a simulation environment using a localization model and by evaluating the effects of configurations on positioning accuracy using computations. Of particular note, we found a "forward-backward configuration effect" from the results and had a detailed a discussion on how it occurred. Finally, a simple experiment using an actual robot validated the effect.

Safety issue has become the primary concern in wheelchair mounted robotic arm applications. We introduced mechanical gravity canceller in the design to realize a light weight manipulator, which also yield the greatly simplify of manipulator dynamics. Based on the simplified dynamics, sensor based contact detection can be easily implemented to enable safety. Contact reaction schemes are also applied through a impedance control law to achieve desired backdrivability. Experiments are conducted to illustrate the proposed method.

In order to relieve the user cognitive burden as well as time consumption of task execution for the "wheelchair mounted robotic arm" application, we proposed a "Task knowledge representation" method that is able to model a large number of tasks coming across in activity of daily livings. Based on the mathematically represented task knowledge, a "Dynamical exploring method" was applied to explore the feasible and optimal robot action sequences that are executed automatically to accomplish the task execution. An integration framework to manage the system complexity is also introduced based on the RT middleware technology. Experiment was conducted to validate our method through the execution of a robotic scenario.

This paper proposes a state identification framework to support the complicated dual-arm operations in construction work. The operational support in construction machinery filed requires the compatibility with different types of support and the commonality among various operator skill levels. The proposed framework is therefore organized into two functions: real-time task phase identification and time-series attentional condition identification. The task phase is defined by utilizing the joint load applied according to the environment constraint condition. The attentional condition is defined as one of the internal work-state condition classified by the necessity level of operational support, and is dependent on the vectorial or time-series date selected by the identified task phase. Experiments are conducted using the hydraulic dual arm system to perform transporting and removing tasks. Results show that the number of error contacts, internal force applied, and mental workload is decreased without time-consumption increase. The result confirmed that the proposed framework greatly contribute to improving each operator's work performance.

This paper reports a newly developed hydraulic dual robotic arm system to create and evaluate intelligent systems, which support complicated machine operations, for advanced construction machinery. This kind of machine system (test-bed) requires functions to quantify its dynamic characteristics and operational difficulty (influential factors). In particular, construction manipulator nonlinearly changes its dynamics depending on various internal and external factors. To quantify such influential factors, our proposed test-bed is equipped with standard hydromechanical system which includes two manipulators and grapples, and practical sensing system which detects control input, oil pressure, oil temperature, and cylinder stroke data. Using the developed test-bed, fundamental experiments were conducted to clarify hydromechanical system characteristics. Experimental results indicate that the test-bed quantifies its hysteresis, pressure loss, and time delay, and show that variable dynamics complicates intuitive and precise machine operations and external force measurement. This analysis confirms the developed machine system is useful to quantify influential factors for creating intelligent system.